diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..7313166d2ed0386d124572322f984f80dc589440 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,160 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Misc +.git +tmp +wandb +data +outputs +.vscode +rl +media + + +# Logging +logs + +# HPC +nautilus/*.yaml +*.key + +# Slurm +sbatch*.sh + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +!tests/artifacts +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Ignore .cache except calibration +.cache/* +!.cache/calibration/ +!.cache/calibration/** + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/.gitattributes b/.gitattributes index 1ef325f1b111266a6b26e0196871bd78baa8c2f3..8c70f8b67773aed980d45d957f20d853027648bd 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,59 +1,31 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.lz4 filter=lfs diff=lfs merge=lfs -text -*.mds filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text -# Audio files - uncompressed -*.pcm filter=lfs diff=lfs merge=lfs -text -*.sam filter=lfs diff=lfs merge=lfs -text -*.raw filter=lfs diff=lfs merge=lfs -text -# Audio files - compressed -*.aac filter=lfs diff=lfs merge=lfs -text -*.flac filter=lfs diff=lfs merge=lfs -text -*.mp3 filter=lfs diff=lfs merge=lfs -text -*.ogg filter=lfs diff=lfs merge=lfs -text -*.wav filter=lfs diff=lfs merge=lfs -text -# Image files - uncompressed -*.bmp filter=lfs diff=lfs merge=lfs -text -*.gif filter=lfs diff=lfs merge=lfs -text -*.png filter=lfs diff=lfs merge=lfs -text -*.tiff filter=lfs diff=lfs merge=lfs -text -# Image files - compressed -*.jpg filter=lfs diff=lfs merge=lfs -text -*.jpeg filter=lfs diff=lfs merge=lfs -text -*.webp filter=lfs diff=lfs merge=lfs -text -# Video files - compressed -*.mp4 filter=lfs diff=lfs merge=lfs -text -*.webm filter=lfs diff=lfs merge=lfs -text +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +*.memmap filter=lfs diff=lfs merge=lfs -text +*.stl filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.json !text !filter !merge !diff +tests/artifacts/cameras/*.png filter=lfs diff=lfs merge=lfs -text +*.bag filter=lfs diff=lfs merge=lfs -text +media/gym/aloha_act.gif filter=lfs diff=lfs merge=lfs -text +media/gym/pusht_diffusion.gif filter=lfs diff=lfs merge=lfs -text +media/gym/simxarm_tdmpc.gif filter=lfs diff=lfs merge=lfs -text +media/lekiwi/kiwi.webp filter=lfs diff=lfs merge=lfs -text +media/lerobot-logo-light.png filter=lfs diff=lfs merge=lfs -text +media/lerobot-logo-thumbnail.png filter=lfs diff=lfs merge=lfs -text +media/so100/leader_follower.webp filter=lfs diff=lfs merge=lfs -text +media/so101/so101-leader.webp filter=lfs diff=lfs merge=lfs -text +media/so101/so101.webp filter=lfs diff=lfs merge=lfs -text +media/wandb.png filter=lfs diff=lfs merge=lfs -text diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 0000000000000000000000000000000000000000..daee5b26814f65a7e2efe085b43235e34403db3d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,68 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: "\U0001F41B Bug Report" +description: Submit a bug report to help us improve LeRobot +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to submit a bug report! 🐛 + If this is not a bug related to the LeRobot library directly, but instead a general question about your code or the library specifically please use our [discord](https://discord.gg/s3KuuzsPFb). + + - type: textarea + id: system-info + attributes: + label: System Info + description: If needed, you can share your lerobot configuration with us by running `python -m lerobot.scripts.display_sys_info` and copy-pasting its outputs below + render: Shell + placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration + validations: + required: true + + - type: checkboxes + id: information-scripts-examples + attributes: + label: Information + description: 'The problem arises when using:' + options: + - label: "One of the scripts in the examples/ folder of LeRobot" + - label: "My own task or dataset (give details below)" + + - type: textarea + id: reproduction + validations: + required: true + attributes: + label: Reproduction + description: | + If needed, provide a simple code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. + Sharing error messages or stack traces could be useful as well! + Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting + Try to avoid screenshots, as they are hard to read and don't allow copy-and-pasting. + + placeholder: | + Steps to reproduce the behavior: + + 1. + 2. + 3. + + - type: textarea + id: expected-behavior + validations: + required: true + attributes: + label: Expected behavior + description: "A clear and concise description of what you would expect to happen." diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000000000000000000000000000000000..bd790597d8adc1f0b24b74c45effaa3b1de260ea --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,34 @@ +## What this does +Explain what this PR does. Feel free to tag your PR with the appropriate label(s). + +Examples: +| Title | Label | +|----------------------|-----------------| +| Fixes #[issue] | (🐛 Bug) | +| Adds new dataset | (🗃️ Dataset) | +| Optimizes something | (⚡️ Performance) | + +## How it was tested +Explain/show how you tested your changes. + +Examples: +- Added `test_something` in `tests/test_stuff.py`. +- Added `new_feature` and checked that training converges with policy X on dataset/environment Y. +- Optimized `some_function`, it now runs X times faster than previously. + +## How to checkout & try? (for the reviewer) +Provide a simple way for the reviewer to try out your changes. + +Examples: +```bash +pytest -sx tests/test_stuff.py::test_something +``` +```bash +python lerobot/scripts/train.py --some.option=true +``` + +## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR +**Note**: Anyone in the community is free to review the PR once the tests have passed. Feel free to tag +members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people. + +**Note**: Before submitting this PR, please read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr). diff --git a/.github/workflows/build-docker-images.yml b/.github/workflows/build-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..1bb9887a4b4917601160fca5ae991cfc41543db8 --- /dev/null +++ b/.github/workflows/build-docker-images.yml @@ -0,0 +1,135 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Inspired by +# https://github.com/huggingface/peft/blob/main/.github/workflows/build_docker_images.yml +name: Builds + +on: + workflow_dispatch: + workflow_call: + schedule: + - cron: "0 1 * * *" + +permissions: {} + +env: + PYTHON_VERSION: "3.10" + +jobs: + latest-cpu: + name: CPU + runs-on: + group: aws-general-8-plus + steps: + - name: Install Git LFS + run: | + sudo apt-get update + sudo apt-get install git-lfs + git lfs install + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0 + with: + cache-binary: false + + - name: Check out code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + lfs: true + persist-credentials: false + + - name: Login to DockerHub + uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + + - name: Build and Push CPU + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0 + with: + context: . + file: ./docker/lerobot-cpu/Dockerfile + push: true + tags: huggingface/lerobot-cpu + build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }} + + + latest-cuda: + name: GPU + runs-on: + group: aws-general-8-plus + steps: + - name: Install Git LFS + run: | + sudo apt-get update + sudo apt-get install git-lfs + git lfs install + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0 + with: + cache-binary: false + + - name: Check out code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + lfs: true + persist-credentials: false + + - name: Login to DockerHub + uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + + - name: Build and Push GPU + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0 + with: + context: . + file: ./docker/lerobot-gpu/Dockerfile + push: true + tags: huggingface/lerobot-gpu + build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }} + + + latest-cuda-dev: + name: GPU Dev + runs-on: + group: aws-general-8-plus + steps: + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0 + with: + cache-binary: false + + - name: Check out code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + persist-credentials: false + + - name: Login to DockerHub + uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + + - name: Build and Push GPU dev + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0 + with: + context: . + file: ./docker/lerobot-gpu-dev/Dockerfile + push: true + tags: huggingface/lerobot-gpu:dev + build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }} diff --git a/.github/workflows/build_documentation.yml b/.github/workflows/build_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..ee1c21742212ecc6fb2864d6730bc78fd58fb4e6 --- /dev/null +++ b/.github/workflows/build_documentation.yml @@ -0,0 +1,23 @@ +name: Build documentation + +on: + workflow_dispatch: + push: + paths: + - "docs/**" + branches: + - main + - doc-builder* + - v*-release + + +jobs: + build: # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers + uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main + with: + commit_sha: ${{ github.sha }} + package: lerobot + additional_args: --not_python_module + secrets: + token: ${{ secrets.HUGGINGFACE_PUSH }} + hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }} diff --git a/.github/workflows/build_pr_documentation.yml b/.github/workflows/build_pr_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..37d614c34363b985b0775a575ad8068582d2c0eb --- /dev/null +++ b/.github/workflows/build_pr_documentation.yml @@ -0,0 +1,19 @@ +name: Build PR Documentation + +on: + pull_request: + paths: + - "docs/**" + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + build: # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers + uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main + with: + commit_sha: ${{ github.event.pull_request.head.sha }} + pr_number: ${{ github.event.number }} + package: lerobot + additional_args: --not_python_module diff --git a/.github/workflows/nightly-tests.yml b/.github/workflows/nightly-tests.yml new file mode 100644 index 0000000000000000000000000000000000000000..8a0c9b076ddc60ef37973b6616c4fe4f79176340 --- /dev/null +++ b/.github/workflows/nightly-tests.yml @@ -0,0 +1,93 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Inspired by +# https://github.com/huggingface/peft/blob/main/.github/workflows/nightly.yml +name: Nightly + +on: + workflow_dispatch: + schedule: + - cron: "0 2 * * *" + +permissions: {} + +# env: + # SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }} +jobs: + run_all_tests_cpu: + name: CPU + strategy: + fail-fast: false + runs-on: + group: aws-general-8-plus + container: + image: huggingface/lerobot-cpu:latest # zizmor: ignore[unpinned-images] + options: --shm-size "16gb" + credentials: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + defaults: + run: + shell: bash + working-directory: /lerobot + steps: + - name: Tests + run: pytest -v --cov=./lerobot --disable-warnings tests + + - name: Tests end-to-end + run: make test-end-to-end + + + run_all_tests_single_gpu: + name: GPU + strategy: + fail-fast: false + runs-on: + group: aws-g6-4xlarge-plus + env: + CUDA_VISIBLE_DEVICES: "0" + TEST_TYPE: "single_gpu" + container: + image: huggingface/lerobot-gpu:latest # zizmor: ignore[unpinned-images] + options: --gpus all --shm-size "16gb" + credentials: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + defaults: + run: + shell: bash + working-directory: /lerobot + steps: + - name: Nvidia-smi + run: nvidia-smi + + - name: Test + run: pytest -v --cov=./lerobot --cov-report=xml --disable-warnings tests + # TODO(aliberts): Link with HF Codecov account + # - name: Upload coverage reports to Codecov with GitHub Action + # uses: codecov/codecov-action@v4 + # with: + # files: ./coverage.xml + # verbose: true + - name: Tests end-to-end + env: + DEVICE: cuda + run: make test-end-to-end + + # - name: Generate Report + # if: always() + # run: | + # pip install slack_sdk tabulate + # python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml new file mode 100644 index 0000000000000000000000000000000000000000..2be0c40a6026c3f73bd3a8d13e8b0219fa96ff9e --- /dev/null +++ b/.github/workflows/quality.yml @@ -0,0 +1,72 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: Quality + +on: + workflow_dispatch: + workflow_call: + pull_request: + push: + branches: + - main + +permissions: {} + +env: + PYTHON_VERSION: "3.10" + +jobs: + style: + name: Style + runs-on: ubuntu-latest + steps: + - name: Checkout Repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1 + with: + python-version: ${{ env.PYTHON_VERSION }} + + - name: Get Ruff Version from pre-commit-config.yaml + id: get-ruff-version + run: | + RUFF_VERSION=$(awk '/repo: https:\/\/github.com\/astral-sh\/ruff-pre-commit/{flag=1;next}/rev:/{if(flag){print $2;exit}}' .pre-commit-config.yaml) + echo "ruff_version=${RUFF_VERSION}" >> $GITHUB_OUTPUT + + - name: Install Ruff + env: + RUFF_VERSION: ${{ steps.get-ruff-version.outputs.ruff_version }} + run: python -m pip install "ruff==${RUFF_VERSION}" + + - name: Ruff check + run: ruff check --output-format=github + + - name: Ruff format + run: ruff format --diff + + typos: + name: Typos + runs-on: ubuntu-latest + steps: + - name: Checkout Repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + persist-credentials: false + + - name: typos-action + uses: crate-ci/typos@db35ee91e80fbb447f33b0e5fbddb24d2a1a884f # v1.29.10 diff --git a/.github/workflows/test-docker-build.yml b/.github/workflows/test-docker-build.yml new file mode 100644 index 0000000000000000000000000000000000000000..c4dbf3e9659595b3a8eb8cc0e372fbba0620f6c1 --- /dev/null +++ b/.github/workflows/test-docker-build.yml @@ -0,0 +1,82 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Inspired by +# https://github.com/huggingface/peft/blob/main/.github/workflows/test-docker-build.yml +name: Test Dockerfiles + +on: + pull_request: + paths: + # Run only when DockerFile files are modified + - "docker/**" + +permissions: {} + +env: + PYTHON_VERSION: "3.10" + +jobs: + get_changed_files: + name: Detect modified Dockerfiles + runs-on: ubuntu-latest + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + steps: + - name: Check out code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + persist-credentials: false + + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42 + with: + files: docker/** + json: "true" + + - name: Run step if only the files listed above change # zizmor: ignore[template-injection] + if: steps.changed-files.outputs.any_changed == 'true' + id: set-matrix + run: | + echo "matrix=${{ steps.changed-files.outputs.all_changed_files}}" >> $GITHUB_OUTPUT + + build_modified_dockerfiles: + name: Build modified Docker images + needs: get_changed_files + runs-on: + group: aws-general-8-plus + if: needs.get_changed_files.outputs.matrix != '' + strategy: + fail-fast: false + matrix: + docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }} + steps: + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0 + with: + cache-binary: false + + - name: Check out code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + persist-credentials: false + + - name: Build Docker image + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0 + with: + file: ${{ matrix.docker-file }} + context: . + push: False + build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000000000000000000000000000000000000..bf80307d09b2274a7169474c16cdaad7d6d5f6d3 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,150 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: Tests + +on: + pull_request: + paths: + - "lerobot/**" + - "tests/**" + - "examples/**" + - ".github/**" + - "pyproject.toml" + - ".pre-commit-config.yaml" + - "Makefile" + - ".cache/**" + push: + branches: + - main + paths: + - "lerobot/**" + - "tests/**" + - "examples/**" + - ".github/**" + - "pyproject.toml" + - ".pre-commit-config.yaml" + - "Makefile" + - ".cache/**" + +permissions: {} + +env: + UV_VERSION: "0.6.0" + +jobs: + pytest: + name: Pytest + runs-on: ubuntu-latest + env: + MUJOCO_GL: egl + steps: + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + lfs: true # Ensure LFS files are pulled + persist-credentials: false + + - name: Install apt dependencies + # portaudio19-dev is needed to install pyaudio + run: | + sudo apt-get update && \ + sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev + + - name: Install uv and python + uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2 + with: + enable-cache: true + version: ${{ env.UV_VERSION }} + python-version: "3.10" + + - name: Install lerobot (all extras) + run: uv sync --all-extras + + - name: Test with pytest + run: | + uv run pytest tests -v --cov=./lerobot --durations=0 \ + -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \ + -W ignore::UserWarning:torch.utils.data.dataloader:558 \ + -W ignore::UserWarning:gymnasium.utils.env_checker:247 \ + && rm -rf tests/outputs outputs + + pytest-minimal: + name: Pytest (minimal install) + runs-on: ubuntu-latest + env: + MUJOCO_GL: egl + steps: + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + lfs: true # Ensure LFS files are pulled + persist-credentials: false + + - name: Install apt dependencies + run: sudo apt-get update && sudo apt-get install -y ffmpeg + + - name: Install uv and python + uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2 + with: + enable-cache: true + version: ${{ env.UV_VERSION }} + python-version: "3.10" + + - name: Install lerobot + run: uv sync --extra "test" + + - name: Test with pytest + run: | + uv run pytest tests -v --cov=./lerobot --durations=0 \ + -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \ + -W ignore::UserWarning:torch.utils.data.dataloader:558 \ + -W ignore::UserWarning:gymnasium.utils.env_checker:247 \ + && rm -rf tests/outputs outputs + + end-to-end: + name: End-to-end + runs-on: ubuntu-latest + env: + MUJOCO_GL: egl + steps: + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + lfs: true # Ensure LFS files are pulled + persist-credentials: false + + - name: Install apt dependencies + # portaudio19-dev is needed to install pyaudio + run: | + sudo apt-get update && \ + sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev + + - name: Install uv and python + uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2 + with: + enable-cache: true + version: ${{ env.UV_VERSION }} + python-version: "3.10" + + - name: Install lerobot (all extras) + run: | + uv venv + uv sync --all-extras + + - name: venv + run: | + echo "PYTHON_PATH=${{ github.workspace }}/.venv/bin/python" >> $GITHUB_ENV + + - name: Test end-to-end + run: | + make test-end-to-end \ + && rm -rf outputs diff --git a/.github/workflows/trufflehog.yml b/.github/workflows/trufflehog.yml new file mode 100644 index 0000000000000000000000000000000000000000..3c4730e823e34d1f2dddd674c67b260a4e4e9501 --- /dev/null +++ b/.github/workflows/trufflehog.yml @@ -0,0 +1,35 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +on: + push: + +name: Secret Leaks + +permissions: {} + +jobs: + trufflehog: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + fetch-depth: 0 + persist-credentials: false + + - name: Secret Scanning + uses: trufflesecurity/trufflehog@90694bf9af66e7536abc5824e7a87246dbf933cb # v3.88.35 + with: + extra_args: --only-verified diff --git a/.github/workflows/upload_pr_documentation.yml b/.github/workflows/upload_pr_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..35cbfd2b1bb0f2b5e4e3251b6ca6b4c51c71bd36 --- /dev/null +++ b/.github/workflows/upload_pr_documentation.yml @@ -0,0 +1,16 @@ +name: Upload PR Documentation + +on: # zizmor: ignore[dangerous-triggers] We follow the same pattern as in Transformers + workflow_run: + workflows: [ "Build PR Documentation" ] + types: + - completed + +jobs: + build: # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers + uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main + with: + package_name: lerobot + secrets: + hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }} + comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e5e4fcf7c30ccb6c14e8f14d74c4069f5084c4e3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,175 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Dev scripts +.dev + +# Logging +logs +tmp +wandb + +# Data +data +outputs + +# Apple +.DS_Store + +# VS Code +.vscode +.devcontainer + +# HPC +nautilus/*.yaml +*.key + +# Slurm +sbatch*.sh + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# uv/poetry lock files +poetry.lock +uv.lock + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +!tests/artifacts +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Ignore .cache +.cache/* + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c9eb8496bfd9d344cc160d6d174bdd83efe8729e --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,74 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +exclude: "tests/artifacts/.*\\.safetensors$" +default_language_version: + python: python3.10 +repos: + ##### Meta ##### + - repo: meta + hooks: + - id: check-useless-excludes + - id: check-hooks-apply + + + ##### Style / Misc. ##### + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-added-large-files + - id: debug-statements + - id: check-merge-conflict + - id: check-case-conflict + - id: check-yaml + - id: check-toml + - id: end-of-file-fixer + - id: trailing-whitespace + + - repo: https://github.com/adhtruong/mirrors-typos + rev: v1.33.1 + hooks: + - id: typos + args: [--force-exclude] + + - repo: https://github.com/asottile/pyupgrade + rev: v3.20.0 + hooks: + - id: pyupgrade + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.13 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + + + ##### Security ##### + - repo: https://github.com/gitleaks/gitleaks + rev: v8.27.2 + hooks: + - id: gitleaks + + - repo: https://github.com/woodruffw/zizmor-pre-commit + rev: v1.9.0 + hooks: + - id: zizmor + + - repo: https://github.com/PyCQA/bandit + rev: 1.8.3 + hooks: + - id: bandit + args: ["-c", "pyproject.toml"] + additional_dependencies: ["bandit[toml]"] diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..97e884ddb34538061ce91c3739b85db5dec6301f --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,133 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official email address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +[feedback@huggingface.co](mailto:feedback@huggingface.co). +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..4fb3d1487a7284d825b51f04ee7f075725e0b3fa --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,305 @@ +# How to contribute to 🤗 LeRobot? + +Everyone is welcome to contribute, and we value everybody's contribution. Code +is thus not the only way to help the community. Answering questions, helping +others, reaching out and improving the documentations are immensely valuable to +the community. + +It also helps us if you spread the word: reference the library from blog posts +on the awesome projects it made possible, shout out on Twitter when it has +helped you, or simply ⭐️ the repo to say "thank you". + +Whichever way you choose to contribute, please be mindful to respect our +[code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md). + +## You can contribute in so many ways! + +Some of the ways you can contribute to 🤗 LeRobot: +* Fixing outstanding issues with the existing code. +* Implementing new models, datasets or simulation environments. +* Contributing to the examples or to the documentation. +* Submitting issues related to bugs or desired new features. + +Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co). + +If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46) + +## Submitting a new issue or feature request + +Do your best to follow these guidelines when submitting an issue or a feature +request. It will make it easier for us to come back to you quickly and with good +feedback. + +### Did you find a bug? + +The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of +the problems they encounter. So thank you for reporting an issue. + +First, we would really appreciate it if you could **make sure the bug was not +already reported** (use the search bar on Github under Issues). + +Did not find it? :( So we can act quickly on it, please follow these steps: + +* Include your **OS type and version**, the versions of **Python** and **PyTorch**. +* A short, self-contained, code snippet that allows us to reproduce the bug in + less than 30s. +* The full traceback if an exception is raised. +* Attach any other additional information, like screenshots, you think may help. + +### Do you want a new feature? + +A good feature request addresses the following points: + +1. Motivation first: +* Is it related to a problem/frustration with the library? If so, please explain + why. Providing a code snippet that demonstrates the problem is best. +* Is it related to something you would need for a project? We'd love to hear + about it! +* Is it something you worked on and think could benefit the community? + Awesome! Tell us what problem it solved for you. +2. Write a *paragraph* describing the feature. +3. Provide a **code snippet** that demonstrates its future use. +4. In case this is related to a paper, please attach a link. +5. Attach any additional information (drawings, screenshots, etc.) you think may help. + +If your issue is well written we're already 80% of the way there by the time you +post it. + +## Adding new policies, datasets or environments + +Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/policies/), +environments ([aloha](https://github.com/huggingface/gym-aloha), +[xarm](https://github.com/huggingface/gym-xarm), +[pusht](https://github.com/huggingface/gym-pusht)) +and follow the same api design. + +When implementing a new dataset loadable with LeRobotDataset follow these steps: +- Update `available_datasets_per_env` in `lerobot/__init__.py` + +When implementing a new environment (e.g. `gym_aloha`), follow these steps: +- Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py` + +When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps: +- Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py` +- Set the required `name` class attribute. +- Update variables in `tests/test_available.py` by importing your new Policy class + +## Submitting a pull request (PR) + +Before writing code, we strongly advise you to search through the existing PRs or +issues to make sure that nobody is already working on the same thing. If you are +unsure, it is always a good idea to open an issue to get some feedback. + +You will need basic `git` proficiency to be able to contribute to +🤗 LeRobot. `git` is not the easiest tool to use but it has the greatest +manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro +Git](https://git-scm.com/book/en/v2) is a very good reference. + +Follow these steps to start contributing: + +1. Fork the [repository](https://github.com/huggingface/lerobot) by + clicking on the 'Fork' button on the repository's page. This creates a copy of the code + under your GitHub user account. + +2. Clone your fork to your local disk, and add the base repository as a remote. The following command + assumes you have your public SSH key uploaded to GitHub. See the following guide for more + [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository). + + ```bash + git clone git@github.com:/lerobot.git + cd lerobot + git remote add upstream https://github.com/huggingface/lerobot.git + ``` + +3. Create a new branch to hold your development changes, and do this for every new PR you work on. + + Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)): + + ```bash + git checkout main + git fetch upstream + git rebase upstream/main + ``` + + Once your `main` branch is synchronized, create a new branch from it: + + ```bash + git checkout -b a-descriptive-name-for-my-changes + ``` + + 🚨 **Do not** work on the `main` branch. + +4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies. + Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already. + + Set up a development environment with conda or miniconda: + ```bash + conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev + ``` + + If you're using `uv`, it can manage python versions so you can instead do: + ```bash + uv venv --python 3.10 && source .venv/bin/activate + ``` + + To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library: + + using `poetry` + ```bash + poetry sync --extras "dev test" + ``` + + using `uv` + ```bash + uv sync --extra dev --extra test + ``` + + You can also install the project with all its dependencies (including environments): + + using `poetry` + ```bash + poetry sync --all-extras + ``` + + using `uv` + ```bash + uv sync --all-extras + ``` + + > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they *will* be tested in the CI. In general, we advise you to install everything and test locally before pushing. + + Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies. + + The equivalent of `pip install some-package`, would just be: + + using `poetry` + ```bash + poetry add some-package + ``` + + using `uv` + ```bash + uv add some-package + ``` + + When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies. + using `poetry` + ```bash + poetry lock + ``` + + using `uv` + ```bash + uv lock + ``` + + +5. Develop the features on your branch. + + As you work on the features, you should make sure that the test suite + passes. You should run the tests impacted by your changes like this (see + below an explanation regarding the environment variable): + + ```bash + pytest tests/.py + ``` + +6. Follow our style. + + `lerobot` relies on `ruff` to format its source code + consistently. Set up [`pre-commit`](https://pre-commit.com/) to run these checks + automatically as Git commit hooks. + + Install `pre-commit` hooks: + ```bash + pre-commit install + ``` + + You can run these hooks whenever you need on staged files with: + ```bash + pre-commit + ``` + + Once you're happy with your changes, add changed files using `git add` and + make a commit with `git commit` to record your changes locally: + + ```bash + git add modified_file.py + git commit + ``` + + Note, if you already committed some changes that have a wrong formatting, you can use: + ```bash + pre-commit run --all-files + ``` + + Please write [good commit messages](https://chris.beams.io/posts/git-commit/). + + It is a good idea to sync your copy of the code with the original + repository regularly. This way you can quickly account for changes: + + ```bash + git fetch upstream + git rebase upstream/main + ``` + + Push the changes to your account using: + + ```bash + git push -u origin a-descriptive-name-for-my-changes + ``` + +6. Once you are satisfied (**and the checklist below is happy too**), go to the + webpage of your fork on GitHub. Click on 'Pull request' to send your changes + to the project maintainers for review. + +7. It's ok if maintainers ask you for changes. It happens to core contributors + too! So everyone can see the changes in the Pull request, work in your local + branch and push the changes to your fork. They will automatically appear in + the pull request. + + +### Checklist + +1. The title of your pull request should be a summary of its contribution; +2. If your pull request addresses an issue, please mention the issue number in + the pull request description to make sure they are linked (and people + consulting the issue know you are working on it); +3. To indicate a work in progress please prefix the title with `[WIP]`, or preferably mark + the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate + it from PRs ready to be merged; +4. Make sure existing tests pass; + +### Tests + +An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/lerobot/tree/main/tests). + +Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already). + +On Mac: +```bash +brew install git-lfs +git lfs install +``` + +On Ubuntu: +```bash +sudo apt-get install git-lfs +git lfs install +``` + +Pull artifacts if they're not in [tests/artifacts](tests/artifacts) +```bash +git lfs pull +``` + +We use `pytest` in order to run the tests. From the root of the +repository, here's how to run tests with `pytest` for the library: + +```bash +python -m pytest -sv ./tests +``` + + +You can specify a smaller set of tests in order to test only the feature +you're working on. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..4e008813bdf5275a2b468feb2f6d63572c54a54a --- /dev/null +++ b/LICENSE @@ -0,0 +1,507 @@ +Copyright 2024 The Hugging Face team. All rights reserved. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + +## Some of lerobot's code is derived from Diffusion Policy, which is subject to the following copyright notice: + +MIT License + +Copyright (c) 2023 Columbia Artificial Intelligence and Robotics Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + + +## Some of lerobot's code is derived from FOWM, which is subject to the following copyright notice: + +MIT License + +Copyright (c) 2023 Yunhai Feng + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + + +## Some of lerobot's code is derived from simxarm, which is subject to the following copyright notice: + +MIT License + +Copyright (c) 2023 Nicklas Hansen & Yanjie Ze + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Zhao + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +## Some of lerobot's code is derived from DETR, which is subject to the following copyright notice: + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2020 - present, Facebook, Inc + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..70f0e4635c57e1d4aaa3b37116d5c975fc40cef4 --- /dev/null +++ b/Makefile @@ -0,0 +1,142 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +.PHONY: tests + +PYTHON_PATH := $(shell which python) + +# If uv is installed and a virtual environment exists, use it +UV_CHECK := $(shell command -v uv) +ifneq ($(UV_CHECK),) + PYTHON_PATH := $(shell .venv/bin/python) +endif + +export PATH := $(dir $(PYTHON_PATH)):$(PATH) + +DEVICE ?= cpu + +build-cpu: + docker build -t lerobot:latest -f docker/lerobot-cpu/Dockerfile . + +build-gpu: + docker build -t lerobot:latest -f docker/lerobot-gpu/Dockerfile . + +test-end-to-end: + ${MAKE} DEVICE=$(DEVICE) test-act-ete-train + ${MAKE} DEVICE=$(DEVICE) test-act-ete-train-resume + ${MAKE} DEVICE=$(DEVICE) test-act-ete-eval + ${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-train + ${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval + ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train + ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval + +test-act-ete-train: + python lerobot/scripts/train.py \ + --policy.type=act \ + --policy.dim_model=64 \ + --policy.n_action_steps=20 \ + --policy.chunk_size=20 \ + --policy.device=$(DEVICE) \ + --env.type=aloha \ + --env.episode_length=5 \ + --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \ + --dataset.image_transforms.enable=true \ + --dataset.episodes="[0]" \ + --batch_size=2 \ + --steps=4 \ + --eval_freq=2 \ + --eval.n_episodes=1 \ + --eval.batch_size=1 \ + --save_freq=2 \ + --save_checkpoint=true \ + --log_freq=1 \ + --wandb.enable=false \ + --output_dir=tests/outputs/act/ + +test-act-ete-train-resume: + python lerobot/scripts/train.py \ + --config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \ + --resume=true + +test-act-ete-eval: + python lerobot/scripts/eval.py \ + --policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \ + --policy.device=$(DEVICE) \ + --env.type=aloha \ + --env.episode_length=5 \ + --eval.n_episodes=1 \ + --eval.batch_size=1 + +test-diffusion-ete-train: + python lerobot/scripts/train.py \ + --policy.type=diffusion \ + --policy.down_dims='[64,128,256]' \ + --policy.diffusion_step_embed_dim=32 \ + --policy.num_inference_steps=10 \ + --policy.device=$(DEVICE) \ + --env.type=pusht \ + --env.episode_length=5 \ + --dataset.repo_id=lerobot/pusht \ + --dataset.image_transforms.enable=true \ + --dataset.episodes="[0]" \ + --batch_size=2 \ + --steps=2 \ + --eval_freq=2 \ + --eval.n_episodes=1 \ + --eval.batch_size=1 \ + --save_checkpoint=true \ + --save_freq=2 \ + --log_freq=1 \ + --wandb.enable=false \ + --output_dir=tests/outputs/diffusion/ + +test-diffusion-ete-eval: + python lerobot/scripts/eval.py \ + --policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \ + --policy.device=$(DEVICE) \ + --env.type=pusht \ + --env.episode_length=5 \ + --eval.n_episodes=1 \ + --eval.batch_size=1 + +test-tdmpc-ete-train: + python lerobot/scripts/train.py \ + --policy.type=tdmpc \ + --policy.device=$(DEVICE) \ + --env.type=xarm \ + --env.task=XarmLift-v0 \ + --env.episode_length=5 \ + --dataset.repo_id=lerobot/xarm_lift_medium \ + --dataset.image_transforms.enable=true \ + --dataset.episodes="[0]" \ + --batch_size=2 \ + --steps=2 \ + --eval_freq=2 \ + --eval.n_episodes=1 \ + --eval.batch_size=1 \ + --save_checkpoint=true \ + --save_freq=2 \ + --log_freq=1 \ + --wandb.enable=false \ + --output_dir=tests/outputs/tdmpc/ + +test-tdmpc-ete-eval: + python lerobot/scripts/eval.py \ + --policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \ + --policy.device=$(DEVICE) \ + --env.type=xarm \ + --env.episode_length=5 \ + --env.task=XarmLift-v0 \ + --eval.n_episodes=1 \ + --eval.batch_size=1 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2efe2a37a7847b6009ad057268336611ac5bc9a0 --- /dev/null +++ b/README.md @@ -0,0 +1,436 @@ +

+ + + + LeRobot, Hugging Face Robotics Library + +
+
+

+ +
+ +[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml?query=branch%3Amain) +[![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot) +[![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/) +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE) +[![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/) +[![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/) +[![Examples](https://img.shields.io/badge/Examples-green.svg)](https://github.com/huggingface/lerobot/tree/main/examples) +[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1%20adopted-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md) +[![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb) + +
+ +

+

+ Build Your Own SO-101 Robot!

+

+ +
+
+ SO-101 follower arm + SO-101 leader arm +
+ + +

Meet the updated SO100, the SO-101 – Just €114 per arm!

+

Train it in minutes with a few simple moves on your laptop.

+

Then sit back and watch your creation act autonomously! 🤯

+ +

+ See the full SO-101 tutorial here.

+ +

Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!

+

Check out the LeKiwi tutorial and bring your robot to life on wheels.

+ + LeKiwi mobile robot +
+ +
+ +

+

LeRobot: State-of-the-art AI for real-world robotics

+

+ +--- + +🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models. + +🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning. + +🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there. + +🤗 LeRobot hosts pretrained models and datasets on this Hugging Face community page: [huggingface.co/lerobot](https://huggingface.co/lerobot) + +#### Examples of pretrained models on simulation environments + + + + + + + + + + + + +
ACT policy on ALOHA envTDMPC policy on SimXArm envDiffusion policy on PushT env
ACT policy on ALOHA envTDMPC policy on SimXArm envDiffusion policy on PushT env
+ +### Acknowledgment + +- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla). +- Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io). +- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io). +- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM). +- Thanks to Antonio Loquercio and Ashish Kumar for their early support. +- Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official). + + +## Installation + +Download our source code: +```bash +git clone https://github.com/huggingface/lerobot.git +cd lerobot +``` + +Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html): +```bash +conda create -y -n lerobot python=3.10 +conda activate lerobot +``` + +When using `miniconda`, install `ffmpeg` in your environment: +```bash +conda install ffmpeg -c conda-forge +``` + +> **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can: +> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using: +> ```bash +> conda install ffmpeg=7.1.1 -c conda-forge +> ``` +> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`. + +Install 🤗 LeRobot: +```bash +pip install -e . +``` + +> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run: +`sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg) + +For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras: +- [aloha](https://github.com/huggingface/gym-aloha) +- [xarm](https://github.com/huggingface/gym-xarm) +- [pusht](https://github.com/huggingface/gym-pusht) + +For instance, to install 🤗 LeRobot with aloha and pusht, use: +```bash +pip install -e ".[aloha, pusht]" +``` + +To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with +```bash +wandb login +``` + +(note: you will also need to enable WandB in the configuration. See below.) + +## Walkthrough + +``` +. +├── examples # contains demonstration examples, start here to learn about LeRobot +| └── advanced # contains even more examples for those who have mastered the basics +├── lerobot +| ├── configs # contains config classes with all options that you can override in the command line +| ├── common # contains classes and utilities +| | ├── datasets # various datasets of human demonstrations: aloha, pusht, xarm +| | ├── envs # various sim environments: aloha, pusht, xarm +| | ├── policies # various policies: act, diffusion, tdmpc +| | ├── robot_devices # various real devices: dynamixel motors, opencv cameras, koch robots +| | └── utils # various utilities +| └── scripts # contains functions to execute via command line +| ├── eval.py # load policy and evaluate it on an environment +| ├── train.py # train a policy via imitation learning and/or reinforcement learning +| ├── control_robot.py # teleoperate a real robot, record data, run a policy +| ├── push_dataset_to_hub.py # convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub +| └── visualize_dataset.py # load a dataset and render its demonstrations +├── outputs # contains results of scripts execution: logs, videos, model checkpoints +└── tests # contains pytest utilities for continuous integration +``` + +### Visualize datasets + +Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub. + +You can also locally visualize episodes from a dataset on the hub by executing our script from the command line: +```bash +python lerobot/scripts/visualize_dataset.py \ + --repo-id lerobot/pusht \ + --episode-index 0 +``` + +or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`) +```bash +python lerobot/scripts/visualize_dataset.py \ + --repo-id lerobot/pusht \ + --root ./my_local_data_dir \ + --local-files-only 1 \ + --episode-index 0 +``` + + +It will open `rerun.io` and display the camera streams, robot states and actions, like this: + +https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144 + + +Our script can also visualize datasets stored on a distant server. See `python lerobot/scripts/visualize_dataset.py --help` for more instructions. + +### The `LeRobotDataset` format + +A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model. + +A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](examples/1_load_lerobot_dataset.py) for more details on `delta_timestamps`. + +Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor. + +Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects: + +``` +dataset attributes: + ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example: + │ ├ observation.images.cam_high (VideoFrame): + │ │ VideoFrame = {'path': path to a mp4 video, 'timestamp' (float32): timestamp in the video} + │ ├ observation.state (list of float32): position of an arm joints (for instance) + │ ... (more observations) + │ ├ action (list of float32): goal position of an arm joints (for instance) + │ ├ episode_index (int64): index of the episode for this sample + │ ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode + │ ├ timestamp (float32): timestamp in the episode + │ ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode + │ └ index (int64): general index in the whole dataset + ├ episode_data_index: contains 2 tensors with the start and end indices of each episode + │ ├ from (1D int64 tensor): first frame index for each episode — shape (num episodes,) starts with 0 + │ └ to: (1D int64 tensor): last frame index for each episode — shape (num episodes,) + ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance + │ ├ observation.images.cam_high: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.} + │ ... + ├ info: a dictionary of metadata on the dataset + │ ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with + │ ├ fps (float): frame per second the dataset is recorded/synchronized to + │ ├ video (bool): indicates if frames are encoded in mp4 video files to save space or stored as png files + │ └ encoding (dict): if video, this documents the main options that were used with ffmpeg to encode the videos + ├ videos_dir (Path): where the mp4 videos or png images are stored/accessed + └ camera_keys (list of string): the keys to access camera features in the item returned by the dataset (e.g. `["observation.images.cam_high", ...]`) +``` + +A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely: +- hf_dataset stored using Hugging Face datasets library serialization to parquet +- videos are stored in mp4 format to save space +- metadata are stored in plain json/jsonl files + +Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location. + +### Evaluate a pretrained policy + +Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrates how to download a pretrained policy from Hugging Face hub, and run an evaluation on its corresponding environment. + +We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht): +```bash +python lerobot/scripts/eval.py \ + --policy.path=lerobot/diffusion_pusht \ + --env.type=pusht \ + --eval.batch_size=10 \ + --eval.n_episodes=10 \ + --policy.use_amp=false \ + --policy.device=cuda +``` + +Note: After training your own policy, you can re-evaluate the checkpoints with: + +```bash +python lerobot/scripts/eval.py --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model +``` + +See `python lerobot/scripts/eval.py --help` for more instructions. + +### Train your own policy + +Check out [example 3](./examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line. + +To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`. + +A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](./examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs. + +![](media/wandb.png) + +Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions. + +#### Reproduce state-of-the-art (SOTA) + +We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances. +You can reproduce their training by loading the config from their run. Simply running: +```bash +python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht +``` +reproduces SOTA results for Diffusion Policy on the PushT task. + +## Contribute + +If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md). + + + + +### Add a pretrained policy + +Once you have trained a policy you may upload it to the Hugging Face hub using a hub id that looks like `${hf_user}/${repo_name}` (e.g. [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)). + +You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain: +- `config.json`: A serialized version of the policy configuration (following the policy's dataclass config). +- `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format. +- `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility. + +To upload these to the hub, run the following: +```bash +huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model +``` + +See [eval.py](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py) for an example of how other people may use your policy. + + +### Improve your code with profiling + +An example of a code snippet to profile the evaluation of a policy: +```python +from torch.profiler import profile, record_function, ProfilerActivity + +def trace_handler(prof): + prof.export_chrome_trace(f"tmp/trace_schedule_{prof.step_num}.json") + +with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + schedule=torch.profiler.schedule( + wait=2, + warmup=2, + active=3, + ), + on_trace_ready=trace_handler +) as prof: + with record_function("eval_policy"): + for i in range(num_episodes): + prof.step() + # insert code to profile, potentially whole body of eval_policy function +``` + +## Citation + +If you want, you can cite this work with: +```bibtex +@misc{cadene2024lerobot, + author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas}, + title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch}, + howpublished = "\url{https://github.com/huggingface/lerobot}", + year = {2024} +} +``` + +Additionally, if you are using any of the particular policy architecture, pretrained models, or datasets, it is recommended to cite the original authors of the work as they appear below: +- [SmolVLA](https://arxiv.org/abs/2506.01844) +```bibtex +@article{shukor2025smolvla, + title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics}, + author={Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and Alibert, Simon and Cord, Matthieu and Wolf, Thomas and Cadene, Remi}, + journal={arXiv preprint arXiv:2506.01844}, + year={2025} +} +``` + +- [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) +```bibtex +@article{chi2024diffusionpolicy, + author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song}, + title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, + journal = {The International Journal of Robotics Research}, + year = {2024}, +} +``` +- [ACT or ALOHA](https://tonyzhaozh.github.io/aloha) +```bibtex +@article{zhao2023learning, + title={Learning fine-grained bimanual manipulation with low-cost hardware}, + author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea}, + journal={arXiv preprint arXiv:2304.13705}, + year={2023} +} +``` + +- [TDMPC](https://www.nicklashansen.com/td-mpc/) + +```bibtex +@inproceedings{Hansen2022tdmpc, + title={Temporal Difference Learning for Model Predictive Control}, + author={Nicklas Hansen and Xiaolong Wang and Hao Su}, + booktitle={ICML}, + year={2022} +} +``` + +- [VQ-BeT](https://sjlee.cc/vq-bet/) +```bibtex +@article{lee2024behavior, + title={Behavior generation with latent actions}, + author={Lee, Seungjae and Wang, Yibin and Etukuru, Haritheja and Kim, H Jin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel}, + journal={arXiv preprint arXiv:2403.03181}, + year={2024} +} +``` + + +- [HIL-SERL](https://hil-serl.github.io/) +```bibtex +@Article{luo2024hilserl, +title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}, +author={Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine}, +year={2024}, +eprint={2410.21845}, +archivePrefix={arXiv}, +primaryClass={cs.RO} +} +``` +## Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline) diff --git a/benchmarks/video/README.md b/benchmarks/video/README.md new file mode 100644 index 0000000000000000000000000000000000000000..59dfeb76e426c754d0a6cde59a616837328a9d5e --- /dev/null +++ b/benchmarks/video/README.md @@ -0,0 +1,271 @@ +# Video benchmark + + +## Questions +What is the optimal trade-off between: +- maximizing loading time with random access, +- minimizing memory space on disk, +- maximizing success rate of policies, +- compatibility across devices/platforms for decoding videos (e.g. video players, web browsers). + +How to encode videos? +- Which video codec (`-vcodec`) to use? h264, h265, AV1? +- What pixel format to use (`-pix_fmt`)? `yuv444p` or `yuv420p`? +- How much compression (`-crf`)? No compression with `0`, intermediate compression with `25` or extreme with `50+`? +- Which frequency to chose for key frames (`-g`)? A key frame every `10` frames? + +How to decode videos? +- Which `decoder`? `torchvision`, `torchaudio`, `ffmpegio`, `decord`, or `nvc`? +- What scenarios to use for the requesting timestamps during benchmark? (`timestamps_mode`) + + +## Variables +**Image content & size** +We don't expect the same optimal settings for a dataset of images from a simulation, or from real-world in an apartment, or in a factory, or outdoor, or with lots of moving objects in the scene, etc. Similarly, loading times might not vary linearly with the image size (resolution). +For these reasons, we run this benchmark on four representative datasets: +- `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera. +- `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera. +- `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera. +- `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera. + +Note: The datasets used for this benchmark need to be image datasets, not video datasets. + +**Data augmentations** +We might revisit this benchmark and find better settings if we train our policies with various data augmentations to make them more robust (e.g. robust to color changes, compression, etc.). + +### Encoding parameters +| parameter | values | +|-------------|--------------------------------------------------------------| +| **vcodec** | `libx264`, `libx265`, `libsvtav1` | +| **pix_fmt** | `yuv444p`, `yuv420p` | +| **g** | `1`, `2`, `3`, `4`, `5`, `6`, `10`, `15`, `20`, `40`, `None` | +| **crf** | `0`, `5`, `10`, `15`, `20`, `25`, `30`, `40`, `50`, `None` | + +Note that `crf` value might be interpreted differently by various video codecs. In other words, the same value used with one codec doesn't necessarily translate into the same compression level with another codec. In fact, the default value (`None`) isn't the same amongst the different video codecs. Importantly, it is also the case for many other ffmpeg arguments like `g` which specifies the frequency of the key frames. + +For a comprehensive list and documentation of these parameters, see the ffmpeg documentation depending on the video codec used: +- h264: https://trac.ffmpeg.org/wiki/Encode/H.264 +- h265: https://trac.ffmpeg.org/wiki/Encode/H.265 +- AV1: https://trac.ffmpeg.org/wiki/Encode/AV1 + +### Decoding parameters +**Decoder** +We tested two video decoding backends from torchvision: +- `pyav` +- `video_reader` (requires to build torchvision from source) + +**Requested timestamps** +Given the way video decoding works, once a keyframe has been loaded, the decoding of subsequent frames is fast. +This of course is affected by the `-g` parameter during encoding, which specifies the frequency of the keyframes. Given our typical use cases in robotics policies which might request a few timestamps in different random places, we want to replicate these use cases with the following scenarios: +- `1_frame`: 1 frame, +- `2_frames`: 2 consecutive frames (e.g. `[t, t + 1 / fps]`), +- `6_frames`: 6 consecutive frames (e.g. `[t + i / fps for i in range(6)]`) + +Note that this differs significantly from a typical use case like watching a movie, in which every frame is loaded sequentially from the beginning to the end and it's acceptable to have big values for `-g`. + +Additionally, because some policies might request single timestamps that are a few frames apart, we also have the following scenario: +- `2_frames_4_space`: 2 frames with 4 consecutive frames of spacing in between (e.g `[t, t + 5 / fps]`), + +However, due to how video decoding is implemented with `pyav`, we don't have access to an accurate seek so in practice this scenario is essentially the same as `6_frames` since all 6 frames between `t` and `t + 5 / fps` will be decoded. + + +## Metrics +**Data compression ratio (lower is better)** +`video_images_size_ratio` is the ratio of the memory space on disk taken by the encoded video over the memory space taken by the original images. For instance, `video_images_size_ratio=25%` means that the video takes 4 times less memory space on disk compared to the original images. + +**Loading time ratio (lower is better)** +`video_images_load_time_ratio` is the ratio of the time it takes to decode frames from the video at a given timestamps over the time it takes to load the exact same original images. Lower is better. For instance, `video_images_load_time_ratio=200%` means that decoding from video is 2 times slower than loading the original images. + +**Average Mean Square Error (lower is better)** +`avg_mse` is the average mean square error between each decoded frame and its corresponding original image over all requested timestamps, and also divided by the number of pixels in the image to be comparable when switching to different image sizes. + +**Average Peak Signal to Noise Ratio (higher is better)** +`avg_psnr` measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR indicates better quality. + +**Average Structural Similarity Index Measure (higher is better)** +`avg_ssim` evaluates the perceived quality of images by comparing luminance, contrast, and structure. SSIM values range from -1 to 1, where 1 indicates perfect similarity. + +One aspect that can't be measured here with those metrics is the compatibility of the encoding across platforms, in particular on web browser, for visualization purposes. +h264, h265 and AV1 are all commonly used codecs and should not pose an issue. However, the chroma subsampling (`pix_fmt`) format might affect compatibility: +- `yuv420p` is more widely supported across various platforms, including web browsers. +- `yuv444p` offers higher color fidelity but might not be supported as broadly. + + + + + +## How the benchmark works +The benchmark evaluates both encoding and decoding of video frames on the first episode of each dataset. + +**Encoding:** for each `vcodec` and `pix_fmt` pair, we use a default value for `g` and `crf` upon which we change a single value (either `g` or `crf`) to one of the specified values (we don't test every combination of those as this would be computationally too heavy). +This gives a unique set of encoding parameters which is used to encode the episode. + +**Decoding:** Then, for each of those unique encodings, we iterate through every combination of the decoding parameters `backend` and `timestamps_mode`. For each of them, we record the metrics of a number of samples (given by `--num-samples`). This is parallelized for efficiency and the number of processes can be controlled with `--num-workers`. Ideally, it's best to have a `--num-samples` that is divisible by `--num-workers`. + +Intermediate results saved for each `vcodec` and `pix_fmt` combination in csv tables. +These are then all concatenated to a single table ready for analysis. + +## Caveats +We tried to measure the most impactful parameters for both encoding and decoding. However, for computational reasons we can't test out every combination. + +Additional encoding parameters exist that are not included in this benchmark. In particular: +- `-preset` which allows for selecting encoding presets. This represents a collection of options that will provide a certain encoding speed to compression ratio. By leaving this parameter unspecified, it is considered to be `medium` for libx264 and libx265 and `8` for libsvtav1. +- `-tune` which allows to optimize the encoding for certain aspects (e.g. film quality, fast decoding, etc.). + +See the documentation mentioned above for more detailed info on these settings and for a more comprehensive list of other parameters. + +Similarly on the decoding side, other decoders exist but are not implemented in our current benchmark. To name a few: +- `torchaudio` +- `ffmpegio` +- `decord` +- `nvc` + +Note as well that since we are mostly interested in the performance at decoding time (also because encoding is done only once before uploading a dataset), we did not measure encoding times nor have any metrics regarding encoding. +However, besides the necessity to build ffmpeg from source, encoding did not pose any issue and it didn't take a significant amount of time during this benchmark. + + +## Install +Building ffmpeg from source is required to include libx265 and libaom/libsvtav1 (av1) video codecs ([compilation guide](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu)). + +**Note:** While you still need to build torchvision with a conda-installed `ffmpeg<4.3` to use the `video_reader` decoder (as described in [#220](https://github.com/huggingface/lerobot/pull/220)), you also need another version which is custom-built with all the video codecs for encoding. For the script to then use that version, you can prepend the command above with `PATH="$HOME/bin:$PATH"`, which is where ffmpeg should be built. + + +## Adding a video decoder +Right now, we're only benchmarking the two video decoder available with torchvision: `pyav` and `video_reader`. +You can easily add a new decoder to benchmark by adding it to this function in the script: +```diff +def decode_video_frames( + video_path: str, + timestamps: list[float], + tolerance_s: float, + backend: str, +) -> torch.Tensor: + if backend in ["pyav", "video_reader"]: + return decode_video_frames_torchvision( + video_path, timestamps, tolerance_s, backend + ) ++ elif backend == ["your_decoder"]: ++ return your_decoder_function( ++ video_path, timestamps, tolerance_s, backend ++ ) + else: + raise NotImplementedError(backend) +``` + + +## Example +For a quick run, you can try these parameters: +```bash +python benchmark/video/run_video_benchmark.py \ + --output-dir outputs/video_benchmark \ + --repo-ids \ + lerobot/pusht_image \ + aliberts/aloha_mobile_shrimp_image \ + --vcodec libx264 libx265 \ + --pix-fmt yuv444p yuv420p \ + --g 2 20 None \ + --crf 10 40 None \ + --timestamps-modes 1_frame 2_frames \ + --backends pyav video_reader \ + --num-samples 5 \ + --num-workers 5 \ + --save-frames 0 +``` + + +## Results + +### Reproduce +We ran the benchmark with the following parameters: +```bash +# h264 and h265 encodings +python benchmark/video/run_video_benchmark.py \ + --output-dir outputs/video_benchmark \ + --repo-ids \ + lerobot/pusht_image \ + aliberts/aloha_mobile_shrimp_image \ + aliberts/paris_street \ + aliberts/kitchen \ + --vcodec libx264 libx265 \ + --pix-fmt yuv444p yuv420p \ + --g 1 2 3 4 5 6 10 15 20 40 None \ + --crf 0 5 10 15 20 25 30 40 50 None \ + --timestamps-modes 1_frame 2_frames 6_frames \ + --backends pyav video_reader \ + --num-samples 50 \ + --num-workers 5 \ + --save-frames 1 + +# av1 encoding (only compatible with yuv420p and pyav decoder) +python benchmark/video/run_video_benchmark.py \ + --output-dir outputs/video_benchmark \ + --repo-ids \ + lerobot/pusht_image \ + aliberts/aloha_mobile_shrimp_image \ + aliberts/paris_street \ + aliberts/kitchen \ + --vcodec libsvtav1 \ + --pix-fmt yuv420p \ + --g 1 2 3 4 5 6 10 15 20 40 None \ + --crf 0 5 10 15 20 25 30 40 50 None \ + --timestamps-modes 1_frame 2_frames 6_frames \ + --backends pyav \ + --num-samples 50 \ + --num-workers 5 \ + --save-frames 1 +``` + +The full results are available [here](https://docs.google.com/spreadsheets/d/1OYJB43Qu8fC26k_OyoMFgGBBKfQRCi4BIuYitQnq3sw/edit?usp=sharing) + + +### Parameters selected for LeRobotDataset +Considering these results, we chose what we think is the best set of encoding parameter: +- vcodec: `libsvtav1` +- pix-fmt: `yuv420p` +- g: `2` +- crf: `30` + +Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_reader` does not support it (and `pyav` doesn't require a custom build of `torchvision`). + +### Summary + +These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav` + +| video_images_size_ratio | vcodec | pix_fmt | | | | +|------------------------------------|------------|---------|-----------|-----------|-----------| +| | libx264 | | libx265 | | libsvtav1 | +| repo_id | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p | +| lerobot/pusht_image | **16.97%** | 17.58% | 18.57% | 18.86% | 22.06% | +| aliberts/aloha_mobile_shrimp_image | 2.14% | 2.11% | 1.38% | **1.37%** | 5.59% | +| aliberts/paris_street | 2.12% | 2.13% | **1.54%** | **1.54%** | 4.43% | +| aliberts/kitchen | 1.40% | 1.39% | **1.00%** | **1.00%** | 2.52% | + +| video_images_load_time_ratio | vcodec | pix_fmt | | | | +|------------------------------------|---------|---------|----------|---------|-----------| +| | libx264 | | libx265 | | libsvtav1 | +| repo_id | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p | +| lerobot/pusht_image | 6.45 | 5.19 | **1.90** | 2.12 | 2.47 | +| aliberts/aloha_mobile_shrimp_image | 11.80 | 7.92 | 0.71 | 0.85 | **0.48** | +| aliberts/paris_street | 2.21 | 2.05 | 0.36 | 0.49 | **0.30** | +| aliberts/kitchen | 1.46 | 1.46 | 0.28 | 0.51 | **0.26** | + +| | | vcodec | pix_fmt | | | | +|------------------------------------|----------|----------|--------------|----------|-----------|--------------| +| | | libx264 | | libx265 | | libsvtav1 | +| repo_id | metric | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p | +| lerobot/pusht_image | avg_mse | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04 | 2.19E-04 | +| | avg_psnr | 35.44 | 37.07 | 35.49 | **37.30** | 37.20 | +| | avg_ssim | 98.28% | **98.85%** | 98.31% | 98.84% | 98.72% | +| aliberts/aloha_mobile_shrimp_image | avg_mse | 2.76E-04 | 2.59E-04 | 3.17E-04 | 3.06E-04 | **1.30E-04** | +| | avg_psnr | 35.91 | 36.21 | 35.88 | 36.09 | **40.17** | +| | avg_ssim | 95.19% | 95.18% | 95.00% | 95.05% | **97.73%** | +| aliberts/paris_street | avg_mse | 6.89E-04 | 6.70E-04 | 4.03E-03 | 4.02E-03 | **3.09E-04** | +| | avg_psnr | 33.48 | 33.68 | 32.05 | 32.15 | **35.40** | +| | avg_ssim | 93.76% | 93.75% | 89.46% | 89.46% | **95.46%** | +| aliberts/kitchen | avg_mse | 2.50E-04 | 2.24E-04 | 4.28E-04 | 4.18E-04 | **1.53E-04** | +| | avg_psnr | 36.73 | 37.33 | 36.56 | 36.75 | **39.12** | +| | avg_ssim | 95.47% | 95.58% | 95.52% | 95.53% | **96.82%** | diff --git a/benchmarks/video/capture_camera_feed.py b/benchmarks/video/capture_camera_feed.py new file mode 100644 index 0000000000000000000000000000000000000000..ccefedc7216d2d3315e263f33fe7ca5af481718e --- /dev/null +++ b/benchmarks/video/capture_camera_feed.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Capture video feed from a camera as raw images.""" + +import argparse +import datetime as dt +import os +import time +from pathlib import Path + +import cv2 +import rerun as rr + +# see https://rerun.io/docs/howto/visualization/limit-ram +RERUN_MEMORY_LIMIT = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "5%") + + +def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height: int, duration: int): + rr.init("lerobot_capture_camera_feed") + rr.spawn(memory_limit=RERUN_MEMORY_LIMIT) + + now = dt.datetime.now() + capture_dir = output_dir / f"{now:%Y-%m-%d}" / f"{now:%H-%M-%S}" + if not capture_dir.exists(): + capture_dir.mkdir(parents=True, exist_ok=True) + + # Opens the default webcam + cap = cv2.VideoCapture(0) + if not cap.isOpened(): + print("Error: Could not open video stream.") + return + + cap.set(cv2.CAP_PROP_FPS, fps) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, width) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height) + + frame_index = 0 + start_time = time.time() + while time.time() - start_time < duration: + ret, frame = cap.read() + + if not ret: + print("Error: Could not read frame.") + break + rr.log("video/stream", rr.Image(frame.numpy()), static=True) + cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame) + frame_index += 1 + + # Release the capture + cap.release() + + # TODO(Steven): Add a graceful shutdown via a close() method for the Viewer context, though not currently supported in the Rerun API. + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--output-dir", + type=Path, + default=Path("outputs/cam_capture/"), + help="Directory where the capture images are written. A subfolder named with the current date & time will be created inside it for each capture.", + ) + parser.add_argument( + "--fps", + type=int, + default=30, + help="Frames Per Second of the capture.", + ) + parser.add_argument( + "--width", + type=int, + default=1280, + help="Width of the captured images.", + ) + parser.add_argument( + "--height", + type=int, + default=720, + help="Height of the captured images.", + ) + parser.add_argument( + "--duration", + type=int, + default=20, + help="Duration in seconds for which the video stream should be captured.", + ) + args = parser.parse_args() + display_and_save_video_stream(**vars(args)) diff --git a/benchmarks/video/run_video_benchmark.py b/benchmarks/video/run_video_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..3f6af2dddd4b49514f6525fb88ee1311451b2f52 --- /dev/null +++ b/benchmarks/video/run_video_benchmark.py @@ -0,0 +1,490 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Assess the performance of video decoding in various configurations. + +This script will benchmark different video encoding and decoding parameters. +See the provided README.md or run `python benchmark/video/run_video_benchmark.py --help` for usage info. +""" + +import argparse +import datetime as dt +import random +import shutil +from collections import OrderedDict +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +import einops +import numpy as np +import pandas as pd +import PIL +import torch +from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity +from tqdm import tqdm + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.video_utils import ( + decode_video_frames_torchvision, + encode_video_frames, +) +from lerobot.common.utils.benchmark import TimeBenchmark + +BASE_ENCODING = OrderedDict( + [ + ("vcodec", "libx264"), + ("pix_fmt", "yuv444p"), + ("g", 2), + ("crf", None), + # TODO(aliberts): Add fastdecode + # ("fastdecode", 0), + ] +) + + +# TODO(rcadene, aliberts): move to `utils.py` folder when we want to refactor +def parse_int_or_none(value) -> int | None: + if value.lower() == "none": + return None + try: + return int(value) + except ValueError as e: + raise argparse.ArgumentTypeError(f"Invalid int or None: {value}") from e + + +def check_datasets_formats(repo_ids: list) -> None: + for repo_id in repo_ids: + dataset = LeRobotDataset(repo_id) + if len(dataset.meta.video_keys) > 0: + raise ValueError( + f"Use only image dataset for running this benchmark. Video dataset provided: {repo_id}" + ) + + +def get_directory_size(directory: Path) -> int: + total_size = 0 + for item in directory.rglob("*"): + if item.is_file(): + total_size += item.stat().st_size + return total_size + + +def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> torch.Tensor: + frames = [] + for ts in timestamps: + idx = int(ts * fps) + frame = PIL.Image.open(imgs_dir / f"frame_{idx:06d}.png") + frame = torch.from_numpy(np.array(frame)) + frame = frame.type(torch.float32) / 255 + frame = einops.rearrange(frame, "h w c -> c h w") + frames.append(frame) + return torch.stack(frames) + + +def save_decoded_frames( + imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int +) -> None: + if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps): + return + + save_dir.mkdir(parents=True, exist_ok=True) + for i, ts in enumerate(timestamps): + idx = int(ts * fps) + frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy() + PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png") + shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png") + + +def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None: + ep_num_images = dataset.episode_data_index["to"][0].item() + if imgs_dir.exists() and len(list(imgs_dir.glob("frame_*.png"))) == ep_num_images: + return + + imgs_dir.mkdir(parents=True, exist_ok=True) + hf_dataset = dataset.hf_dataset.with_format(None) + + # We only save images from the first camera + img_keys = [key for key in hf_dataset.features if key.startswith("observation.image")] + imgs_dataset = hf_dataset.select_columns(img_keys[0]) + + for i, item in enumerate( + tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False) + ): + img = item[img_keys[0]] + img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100) + + if i >= ep_num_images - 1: + break + + +def sample_timestamps(timestamps_mode: str, ep_num_images: int, fps: int) -> list[float]: + # Start at 5 to allow for 2_frames_4_space and 6_frames + idx = random.randint(5, ep_num_images - 1) + match timestamps_mode: + case "1_frame": + frame_indexes = [idx] + case "2_frames": + frame_indexes = [idx - 1, idx] + case "2_frames_4_space": + frame_indexes = [idx - 5, idx] + case "6_frames": + frame_indexes = [idx - i for i in range(6)][::-1] + case _: + raise ValueError(timestamps_mode) + + return [idx / fps for idx in frame_indexes] + + +def decode_video_frames( + video_path: str, + timestamps: list[float], + tolerance_s: float, + backend: str, +) -> torch.Tensor: + if backend in ["pyav", "video_reader"]: + return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend) + else: + raise NotImplementedError(backend) + + +def benchmark_decoding( + imgs_dir: Path, + video_path: Path, + timestamps_mode: str, + backend: str, + ep_num_images: int, + fps: int, + num_samples: int = 50, + num_workers: int = 4, + save_frames: bool = False, +) -> dict: + def process_sample(sample: int): + time_benchmark = TimeBenchmark() + timestamps = sample_timestamps(timestamps_mode, ep_num_images, fps) + num_frames = len(timestamps) + result = { + "psnr_values": [], + "ssim_values": [], + "mse_values": [], + } + + with time_benchmark: + frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend) + result["load_time_video_ms"] = time_benchmark.result_ms / num_frames + + with time_benchmark: + original_frames = load_original_frames(imgs_dir, timestamps, fps) + result["load_time_images_ms"] = time_benchmark.result_ms / num_frames + + frames_np, original_frames_np = frames.numpy(), original_frames.numpy() + for i in range(num_frames): + result["mse_values"].append(mean_squared_error(original_frames_np[i], frames_np[i])) + result["psnr_values"].append( + peak_signal_noise_ratio(original_frames_np[i], frames_np[i], data_range=1.0) + ) + result["ssim_values"].append( + structural_similarity(original_frames_np[i], frames_np[i], data_range=1.0, channel_axis=0) + ) + + if save_frames and sample == 0: + save_dir = video_path.with_suffix("") / f"{timestamps_mode}_{backend}" + save_decoded_frames(imgs_dir, save_dir, frames, timestamps, fps) + + return result + + load_times_video_ms = [] + load_times_images_ms = [] + mse_values = [] + psnr_values = [] + ssim_values = [] + + # A sample is a single set of decoded frames specified by timestamps_mode (e.g. a single frame, 2 frames, etc.). + # For each sample, we record metrics (loading time and quality metrics) which are then averaged over all samples. + # As these samples are independent, we run them in parallel threads to speed up the benchmark. + with ThreadPoolExecutor(max_workers=num_workers) as executor: + futures = [executor.submit(process_sample, i) for i in range(num_samples)] + for future in tqdm(as_completed(futures), total=num_samples, desc="samples", leave=False): + result = future.result() + load_times_video_ms.append(result["load_time_video_ms"]) + load_times_images_ms.append(result["load_time_images_ms"]) + psnr_values.extend(result["psnr_values"]) + ssim_values.extend(result["ssim_values"]) + mse_values.extend(result["mse_values"]) + + avg_load_time_video_ms = float(np.array(load_times_video_ms).mean()) + avg_load_time_images_ms = float(np.array(load_times_images_ms).mean()) + video_images_load_time_ratio = avg_load_time_video_ms / avg_load_time_images_ms + + return { + "avg_load_time_video_ms": avg_load_time_video_ms, + "avg_load_time_images_ms": avg_load_time_images_ms, + "video_images_load_time_ratio": video_images_load_time_ratio, + "avg_mse": float(np.mean(mse_values)), + "avg_psnr": float(np.mean(psnr_values)), + "avg_ssim": float(np.mean(ssim_values)), + } + + +def benchmark_encoding_decoding( + dataset: LeRobotDataset, + video_path: Path, + imgs_dir: Path, + encoding_cfg: dict, + decoding_cfg: dict, + num_samples: int, + num_workers: int, + save_frames: bool, + overwrite: bool = False, + seed: int = 1337, +) -> list[dict]: + fps = dataset.fps + + if overwrite or not video_path.is_file(): + tqdm.write(f"encoding {video_path}") + encode_video_frames( + imgs_dir=imgs_dir, + video_path=video_path, + fps=fps, + vcodec=encoding_cfg["vcodec"], + pix_fmt=encoding_cfg["pix_fmt"], + g=encoding_cfg.get("g"), + crf=encoding_cfg.get("crf"), + # fast_decode=encoding_cfg.get("fastdecode"), + overwrite=True, + ) + + ep_num_images = dataset.episode_data_index["to"][0].item() + width, height = tuple(dataset[0][dataset.meta.camera_keys[0]].shape[-2:]) + num_pixels = width * height + video_size_bytes = video_path.stat().st_size + images_size_bytes = get_directory_size(imgs_dir) + video_images_size_ratio = video_size_bytes / images_size_bytes + + random.seed(seed) + benchmark_table = [] + for timestamps_mode in tqdm( + decoding_cfg["timestamps_modes"], desc="decodings (timestamps_modes)", leave=False + ): + for backend in tqdm(decoding_cfg["backends"], desc="decodings (backends)", leave=False): + benchmark_row = benchmark_decoding( + imgs_dir, + video_path, + timestamps_mode, + backend, + ep_num_images, + fps, + num_samples, + num_workers, + save_frames, + ) + benchmark_row.update( + **{ + "repo_id": dataset.repo_id, + "resolution": f"{width} x {height}", + "num_pixels": num_pixels, + "video_size_bytes": video_size_bytes, + "images_size_bytes": images_size_bytes, + "video_images_size_ratio": video_images_size_ratio, + "timestamps_mode": timestamps_mode, + "backend": backend, + }, + **encoding_cfg, + ) + benchmark_table.append(benchmark_row) + + return benchmark_table + + +def main( + output_dir: Path, + repo_ids: list[str], + vcodec: list[str], + pix_fmt: list[str], + g: list[int], + crf: list[int], + # fastdecode: list[int], + timestamps_modes: list[str], + backends: list[str], + num_samples: int, + num_workers: int, + save_frames: bool, +): + check_datasets_formats(repo_ids) + encoding_benchmarks = { + "g": g, + "crf": crf, + # "fastdecode": fastdecode, + } + decoding_benchmarks = { + "timestamps_modes": timestamps_modes, + "backends": backends, + } + headers = ["repo_id", "resolution", "num_pixels"] + headers += list(BASE_ENCODING.keys()) + headers += [ + "timestamps_mode", + "backend", + "video_size_bytes", + "images_size_bytes", + "video_images_size_ratio", + "avg_load_time_video_ms", + "avg_load_time_images_ms", + "video_images_load_time_ratio", + "avg_mse", + "avg_psnr", + "avg_ssim", + ] + file_paths = [] + for video_codec in tqdm(vcodec, desc="encodings (vcodec)"): + for pixel_format in tqdm(pix_fmt, desc="encodings (pix_fmt)", leave=False): + benchmark_table = [] + for repo_id in tqdm(repo_ids, desc="encodings (datasets)", leave=False): + dataset = LeRobotDataset(repo_id) + imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_") + # We only use the first episode + save_first_episode(imgs_dir, dataset) + for key, values in tqdm(encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False): + for value in tqdm(values, desc=f"encodings ({key})", leave=False): + encoding_cfg = BASE_ENCODING.copy() + encoding_cfg["vcodec"] = video_codec + encoding_cfg["pix_fmt"] = pixel_format + encoding_cfg[key] = value + args_path = Path("_".join(str(value) for value in encoding_cfg.values())) + video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4" + benchmark_table += benchmark_encoding_decoding( + dataset, + video_path, + imgs_dir, + encoding_cfg, + decoding_benchmarks, + num_samples, + num_workers, + save_frames, + ) + + # Save intermediate results + benchmark_df = pd.DataFrame(benchmark_table, columns=headers) + now = dt.datetime.now() + csv_path = ( + output_dir + / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_{video_codec}_{pixel_format}_{num_samples}-samples.csv" + ) + benchmark_df.to_csv(csv_path, header=True, index=False) + file_paths.append(csv_path) + del benchmark_df + + # Concatenate all results + df_list = [pd.read_csv(csv_path) for csv_path in file_paths] + concatenated_df = pd.concat(df_list, ignore_index=True) + concatenated_path = output_dir / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_all_{num_samples}-samples.csv" + concatenated_df.to_csv(concatenated_path, header=True, index=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--output-dir", + type=Path, + default=Path("outputs/video_benchmark"), + help="Directory where the video benchmark outputs are written.", + ) + parser.add_argument( + "--repo-ids", + type=str, + nargs="*", + default=[ + "lerobot/pusht_image", + "aliberts/aloha_mobile_shrimp_image", + "aliberts/paris_street", + "aliberts/kitchen", + ], + help="Datasets repo-ids to test against. First episodes only are used. Must be images.", + ) + parser.add_argument( + "--vcodec", + type=str, + nargs="*", + default=["libx264", "hevc", "libsvtav1"], + help="Video codecs to be tested", + ) + parser.add_argument( + "--pix-fmt", + type=str, + nargs="*", + default=["yuv444p", "yuv420p"], + help="Pixel formats (chroma subsampling) to be tested", + ) + parser.add_argument( + "--g", + type=parse_int_or_none, + nargs="*", + default=[1, 2, 3, 4, 5, 6, 10, 15, 20, 40, 100, None], + help="Group of pictures sizes to be tested.", + ) + parser.add_argument( + "--crf", + type=parse_int_or_none, + nargs="*", + default=[0, 5, 10, 15, 20, 25, 30, 40, 50, None], + help="Constant rate factors to be tested.", + ) + # parser.add_argument( + # "--fastdecode", + # type=int, + # nargs="*", + # default=[0, 1], + # help="Use the fastdecode tuning option. 0 disables it. " + # "For libx264 and libx265/hevc, only 1 is possible. " + # "For libsvtav1, 1, 2 or 3 are possible values with a higher number meaning a faster decoding optimization", + # ) + parser.add_argument( + "--timestamps-modes", + type=str, + nargs="*", + default=[ + "1_frame", + "2_frames", + "2_frames_4_space", + "6_frames", + ], + help="Timestamps scenarios to be tested.", + ) + parser.add_argument( + "--backends", + type=str, + nargs="*", + default=["pyav", "video_reader"], + help="Torchvision decoding backend to be tested.", + ) + parser.add_argument( + "--num-samples", + type=int, + default=50, + help="Number of samples for each encoding x decoding config.", + ) + parser.add_argument( + "--num-workers", + type=int, + default=10, + help="Number of processes for parallelized sample processing.", + ) + parser.add_argument( + "--save-frames", + type=int, + default=0, + help="Whether to save decoded frames or not. Enter a non-zero number for true.", + ) + args = parser.parse_args() + main(**vars(args)) diff --git a/docker/lerobot-cpu/Dockerfile b/docker/lerobot-cpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a6d384dc0bedd45f8d29851d7d3aa5035413edf6 --- /dev/null +++ b/docker/lerobot-cpu/Dockerfile @@ -0,0 +1,29 @@ +# Configure image +ARG PYTHON_VERSION=3.10 +FROM python:${PYTHON_VERSION}-slim + +# Configure environment variables +ARG PYTHON_VERSION +ENV DEBIAN_FRONTEND=noninteractive +ENV MUJOCO_GL="egl" +ENV PATH="/opt/venv/bin:$PATH" + +# Install dependencies and set up Python in a single layer +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential cmake git \ + libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \ + speech-dispatcher libgeos-dev \ + && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \ + && python -m venv /opt/venv \ + && apt-get clean && rm -rf /var/lib/apt/lists/* \ + && echo "source /opt/venv/bin/activate" >> /root/.bashrc + +# Clone repository and install LeRobot in a single layer +COPY . /lerobot +WORKDIR /lerobot +RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \ + && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht]" \ + --extra-index-url https://download.pytorch.org/whl/cpu + +# Execute in bash shell rather than python +CMD ["/bin/bash"] diff --git a/docker/lerobot-gpu-dev/Dockerfile b/docker/lerobot-gpu-dev/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..908faab833056923b9c57321baa0476a7a15375c --- /dev/null +++ b/docker/lerobot-gpu-dev/Dockerfile @@ -0,0 +1,68 @@ +FROM nvidia/cuda:12.2.2-devel-ubuntu22.04 + +# Configure image +ARG PYTHON_VERSION=3.10 +ARG DEBIAN_FRONTEND=noninteractive + +# Install apt dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential cmake \ + git git-lfs openssh-client \ + nano vim less util-linux tree \ + htop atop nvtop \ + sed gawk grep curl wget zip unzip \ + tcpdump sysstat screen tmux \ + libglib2.0-0 libgl1-mesa-glx libegl1-mesa \ + speech-dispatcher portaudio19-dev libgeos-dev \ + python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \ + && apt-get clean && rm -rf /var/lib/apt/lists/* + +# Install ffmpeg build dependencies. See: +# https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu +# TODO(aliberts): create image to build dependencies from source instead +RUN apt-get update && apt-get install -y --no-install-recommends \ + autoconf automake yasm \ + libass-dev \ + libfreetype6-dev \ + libgnutls28-dev \ + libunistring-dev \ + libmp3lame-dev \ + libtool \ + libvorbis-dev \ + meson \ + ninja-build \ + pkg-config \ + texinfo \ + yasm \ + zlib1g-dev \ + nasm \ + libx264-dev \ + libx265-dev libnuma-dev \ + libvpx-dev \ + libfdk-aac-dev \ + libopus-dev \ + libsvtav1-dev libsvtav1enc-dev libsvtav1dec-dev \ + libdav1d-dev + +# Install gh cli tool +RUN (type -p wget >/dev/null || (apt update && apt-get install wget -y)) \ + && mkdir -p -m 755 /etc/apt/keyrings \ + && wget -qO- https://cli.github.com/packages/githubcli-archive-keyring.gpg | tee /etc/apt/keyrings/githubcli-archive-keyring.gpg > /dev/null \ + && chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg \ + && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \ + && apt update \ + && apt install gh -y \ + && apt clean && rm -rf /var/lib/apt/lists/* + +# Setup `python` +RUN ln -s /usr/bin/python3 /usr/bin/python + +# Install poetry +RUN curl -sSL https://install.python-poetry.org | python - +ENV PATH="/root/.local/bin:$PATH" +RUN echo 'if [ "$HOME" != "/root" ]; then ln -sf /root/.local/bin/poetry $HOME/.local/bin/poetry; fi' >> /root/.bashrc +RUN poetry config virtualenvs.create false +RUN poetry config virtualenvs.in-project true + +# Set EGL as the rendering backend for MuJoCo +ENV MUJOCO_GL="egl" diff --git a/docker/lerobot-gpu/Dockerfile b/docker/lerobot-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..2430ee4defebad49acb6b708807f77925c15c396 --- /dev/null +++ b/docker/lerobot-gpu/Dockerfile @@ -0,0 +1,24 @@ +FROM nvidia/cuda:12.4.1-base-ubuntu22.04 + +# Configure environment variables +ARG PYTHON_VERSION=3.10 +ENV DEBIAN_FRONTEND=noninteractive +ENV MUJOCO_GL="egl" +ENV PATH="/opt/venv/bin:$PATH" + +# Install dependencies and set up Python in a single layer +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential cmake git \ + libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \ + speech-dispatcher libgeos-dev \ + python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \ + && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \ + && python -m venv /opt/venv \ + && apt-get clean && rm -rf /var/lib/apt/lists/* \ + && echo "source /opt/venv/bin/activate" >> /root/.bashrc + +# Clone repository and install LeRobot in a single layer +COPY . /lerobot +WORKDIR /lerobot +RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \ + && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]" diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..490c86bc08fec569ba378b2db56fd3a15275f3ab --- /dev/null +++ b/docs/README.md @@ -0,0 +1,137 @@ + + +# Generating the documentation + +To generate the documentation, you first have to build it. Several packages are necessary to build the doc, +you can install them with the following command, at the root of the code repository: + +```bash +pip install -e ".[docs]" +``` + +You will also need `nodejs`. Please refer to their [installation page](https://nodejs.org/en/download) + +--- +**NOTE** + +You only need to generate the documentation to inspect it locally (if you're planning changes and want to +check how they look before committing for instance). You don't have to `git commit` the built documentation. + +--- + +## Building the documentation + +Once you have setup the `doc-builder` and additional packages, you can generate the documentation by +typing the following command: + +```bash +doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build +``` + +You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate +the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite +Markdown editor. + +## Previewing the documentation + +To preview the docs, first install the `watchdog` module with: + +```bash +pip install watchdog +``` + +Then run the following command: + +```bash +doc-builder preview lerobot docs/source/ +``` + +The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives. + +--- +**NOTE** + +The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again). + +--- + +## Adding a new element to the navigation bar + +Accepted files are Markdown (.md). + +Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting +the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/lerobot/blob/main/docs/source/_toctree.yml) file. + +## Renaming section headers and moving sections + +It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information. + +Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor. + +So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file: + +``` +Sections that were moved: + +[ Section A ] +``` +and of course, if you moved it to another file, then: + +``` +Sections that were moved: + +[ Section A ] +``` + +Use the relative style to link to the new file so that the versioned docs continue to work. + +For an example of a rich moved sections set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md). + +### Adding a new tutorial + +Adding a new tutorial or section is done in two steps: + +- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md). +- Link that file in `./source/_toctree.yml` on the correct toc-tree. + +Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR. + +### Writing source documentation + +Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names +and objects like True, None or any strings should usually be put in `code`. + +#### Writing a multi-line code block + +Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown: + + +```` +``` +# first line of code +# second line +# etc +``` +```` + +#### Adding an image + +Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like +the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference +them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). +If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images +to this dataset. diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml new file mode 100644 index 0000000000000000000000000000000000000000..824c258470e1f182fbc1ea10e93db4500ce8a131 --- /dev/null +++ b/docs/source/_toctree.yml @@ -0,0 +1,44 @@ +- sections: + - local: index + title: LeRobot + - local: installation + title: Installation + title: Get started +- sections: + - local: il_robots + title: Imitation Learning for Robots + - local: il_sim + title: Imitation Learning in Sim + - local: cameras + title: Cameras + - local: integrate_hardware + title: Bring Your Own Hardware + - local: hilserl + title: Train a Robot with RL + - local: hilserl_sim + title: Train RL in Simulation + title: "Tutorials" +- sections: + - local: smolvla + title: Finetune SmolVLA + title: "Policies" +- sections: + - local: so101 + title: SO-101 + - local: so100 + title: SO-100 + - local: koch + title: Koch v1.1 + - local: lekiwi + title: LeKiwi + title: "Robots" +- sections: + - local: notebooks + title: Notebooks + title: "Resources" +- sections: + - local: contributing + title: Contribute to LeRobot + - local: backwardcomp + title: Backward compatibility + title: "About" diff --git a/docs/source/backwardcomp.mdx b/docs/source/backwardcomp.mdx new file mode 100644 index 0000000000000000000000000000000000000000..332bdbcb5667b51c5d91a609647837b2de3d7293 --- /dev/null +++ b/docs/source/backwardcomp.mdx @@ -0,0 +1,82 @@ +# Backward compatibility + +## Hardware API redesign + +PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request. + +### What changed? + +| | Before PR #777 | After PR #777 | +| --------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------- | +| **Joint range** | Degrees `-180...180°` | **Normalised range** Joints: `–100...100` Gripper: `0...100` | +| **Zero position (SO100 / SO101)** | Arm fully extended horizontally | **In middle of the range for each joint** | +| **Boundary handling** | Software safeguards to detect ±180 ° wrap-arounds | No wrap-around logic needed due to mid-range zero | + +--- + +### Impact on existing datasets + +* Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly: + * Joint angles are offset and incorrectly normalized. +* Any models directly finetuned or trained on the old data will need their inputs and outputs converted. + +### Using datasets made with the previous calibration system +We provide a migration example script for replaying an episode recorded with the previous calibration here: `examples/backward_compatibility/replay.py`. +Below we take you through the modifications that are done in the example script to make the previous calibration datasets work. + +```diff ++ key = f"{name.removeprefix('main_')}.pos" + action[key] = action_array[i].item() ++ action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90) ++ action["elbow_flex.pos"] -= 90 +``` + +Let's break this down. +New codebase uses `.pos` suffix for the position observations and we have removed `main_` prefix: +```python +key = f"{name.removeprefix('main_')}.pos" +``` + +For `"shoulder_lift"` (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code. +```python +action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90) +``` +For `"elbow_flex"` (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code. +```python +action["elbow_flex.pos"] -= 90 +``` + +To use degrees normalization we then set the `--robot.use_degrees` option to `true`. +```diff +python examples/backward_compatibility/replay.py \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem5A460814411 \ + --robot.id=blue \ ++ --robot.use_degrees=true \ + --dataset.repo_id=my_dataset_id \ + --dataset.episode=0 +``` + +### Using policies trained with the previous calibration system + +Policies output actions in the same format as the datasets (`torch.Tensors`). Therefore, the same transformations should be applied. + +To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above. +Then, add these same transformations on your inference script (shown here in the `record.py` script): +```diff +action_values = predict_action( + observation_frame, + policy, + get_safe_torch_device(policy.config.device), + policy.config.use_amp, + task=single_task, + robot_type=robot.robot_type, + ) + action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)} + ++ action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90) ++ action["elbow_flex.pos"] -= 90 + robot.send_action(action) +``` + +If you have questions or run into migration issues, feel free to ask them on [Discord](https://discord.gg/s3KuuzsPFb) diff --git a/docs/source/cameras.mdx b/docs/source/cameras.mdx new file mode 100644 index 0000000000000000000000000000000000000000..a82843931c85b14bdba21d1d3dd24f5a9a389a2e --- /dev/null +++ b/docs/source/cameras.mdx @@ -0,0 +1,173 @@ +# Cameras + +LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html). + +### Finding your camera + +To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system. + +To find the camera indices of the cameras plugged into your system, run the following script: +```bash +python lerobot/find_cameras.py opencv # or realsense for Intel Realsense cameras +``` + +The output will look something like this if you have two cameras connected: +``` +--- Detected Cameras --- +Camera #0: + Name: OpenCV Camera @ 0 + Type: OpenCV + Id: 0 + Backend api: AVFOUNDATION + Default stream profile: + Format: 16.0 + Width: 1920 + Height: 1080 + Fps: 15.0 +-------------------- +(more cameras ...) +``` + +> [!WARNING] +> When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable. + + +## Use Cameras + +Below are two examples, demonstrating how to work with the API. + +- **Asynchronous frame capture** using an OpenCV-based camera +- **Color and depth capture** using an Intel RealSense camera + + + + + +```python +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera +from lerobot.common.cameras.configs import ColorMode, Cv2Rotation + +# Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation. +config = OpenCVCameraConfig( + index_or_path=0, + fps=15, + width=1920, + height=1080, + color_mode=ColorMode.RGB, + rotation=Cv2Rotation.NO_ROTATION +) + +# Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default). +camera = OpenCVCamera(config) +camera.connect() + +# Read frames asynchronously in a loop via `async_read(timeout_ms)` +try: + for i in range(10): + frame = camera.async_read(timeout_ms=200) + print(f"Async frame {i} shape:", frame.shape) +finally: + camera.disconnect() +``` + + + + +```python +from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig +from lerobot.common.cameras.realsense.camera_realsense import RealSenseCamera +from lerobot.common.cameras.configs import ColorMode, Cv2Rotation + +# Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth. +config = RealSenseCameraConfig( + serial_number_or_name="233522074606", + fps=15, + width=640, + height=480, + color_mode=ColorMode.RGB, + use_depth=True, + rotation=Cv2Rotation.NO_ROTATION +) + +# Instantiate and connect a `RealSenseCamera` with warm-up read (default). +camera = RealSenseCamera(config) +camera.connect() + +# Capture a color frame via `read()` and a depth map via `read_depth()`. +try: + color_frame = camera.read() + depth_map = camera.read_depth() + print("Color frame shape:", color_frame.shape) + print("Depth map shape:", depth_map.shape) +finally: + camera.disconnect() +``` + + + + +## Use your phone + + + +To use your iPhone as a camera on macOS, enable the Continuity Camera feature: +- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later. +- Sign in both devices with the same Apple ID. +- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection. + +For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac). + +Your iPhone should be detected automatically when running the camera setup script in the next section. + + + + +If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera + +1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using: +```python +sudo apt install v4l2loopback-dkms v4l-utils +``` +2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android. +3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org): +```python +flatpak install flathub com.obsproject.Studio +``` +4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with: +```python +flatpak install flathub com.obsproject.Studio.Plugin.DroidCam +``` +5. *Start OBS Studio*. Launch with: +```python +flatpak run com.obsproject.Studio +``` +6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`. +7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in. +8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide). +9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices: +```python +v4l2-ctl --list-devices +``` +You should see an entry like: +``` +VirtualCam (platform:v4l2loopback-000): +/dev/video1 +``` +10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`. +```python +v4l2-ctl -d /dev/video1 --get-fmt-video +``` +You should see an entry like: +``` +>>> Format Video Capture: +>>> Width/Height : 640/480 +>>> Pixel Format : 'YUYV' (YUYV 4:2:2) +``` + +Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed. + +If everything is set up correctly, you can proceed with the rest of the tutorial. + + + diff --git a/docs/source/contributing.md b/docs/source/contributing.md new file mode 100644 index 0000000000000000000000000000000000000000..f939e75f21a8badb5c40f527abd0e098fe9bc472 --- /dev/null +++ b/docs/source/contributing.md @@ -0,0 +1 @@ +../../CONTRIBUTING.md \ No newline at end of file diff --git a/docs/source/hilserl.mdx b/docs/source/hilserl.mdx new file mode 100644 index 0000000000000000000000000000000000000000..7383d824dec13252c23f519b439dc8699b3108c4 --- /dev/null +++ b/docs/source/hilserl.mdx @@ -0,0 +1,547 @@ +# HIL-SERL Real Robot Training Workflow Guide + +In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient Reinforcement Learning (HIL-SERL) workflow using LeRobot. You will master training a policy with RL on a real robot in just a few hours. + +HIL-SERL is a sample-efficient reinforcement learning algorithm that combines human demonstrations with online learning and human interventions. The approach starts from a small set of human demonstrations, uses them to train a reward classifier, and then employs an actor-learner architecture where humans can intervene during policy execution to guide exploration and correct unsafe behaviors. In this tutorial, you'll use a gamepad to provide interventions and control the robot during the learning process. + +It combines three key ingredients: + 1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point. + 2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour. + 3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe. + +Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines. + +

+ HIL-SERL workflow +

+ +

HIL-SERL workflow, Luo et al. 2024

+ +This guide provides step-by-step instructions for training a robot policy using LeRobot's HilSerl implementation to train on a real robot. + +## What do I need? + +- A gamepad (recommended) or keyboard to control the robot +- A Nvidia GPU +- A real robot with a follower and leader arm (optional if you use the keyboard or the gamepad) + +## What kind of tasks can I train? + +One can use HIL-SERL to train on a variety of manipulation tasks. Some recommendations: +- Start with a simple task to understand how the system works. + - Push cube to a goal region + - Pick and lift cube with the gripper +- Avoid extremely long horizon tasks. Focus on tasks that can be completed in 5-10 seconds. +- Once you have a good idea of how the system works, you can try more complex tasks and longer horizons. + - Pick and place cube + - Bimanual tasks to pick objects with two arms + - Hand-over tasks to transfer objects from one arm to another + - Go crazy! + +## Install LeRobot with HIL-SERL + +To install LeRobot with HIL-SERL, you need to install the `hilserl` extra. + +```bash +pip install -e ".[hilserl]" +``` + +## Real Robot Training Workflow + +### Understanding Configuration + +The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/common/envs/configs.py`. Which is defined as: + +```python +class HILSerlRobotEnvConfig(EnvConfig): + robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/common/robots`) + teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/common/teleoperators`) + wrapper: EnvTransformConfig | None = None # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py` + fps: int = 10 # Control frequency + name: str = "real_robot" # Environment name + mode: str = None # "record", "replay", or None (for training) + repo_id: str | None = None # LeRobot dataset repository ID + dataset_root: str | None = None # Local dataset root (optional) + task: str = "" # Task identifier + num_episodes: int = 10 # Number of episodes for recording + episode: int = 0 # episode index for replay + device: str = "cuda" # Compute device + push_to_hub: bool = True # Whether to push the recorded datasets to Hub + pretrained_policy_name_or_path: str | None = None # For policy loading + reward_classifier_pretrained_path: str | None = None # For reward model + number_of_steps_after_success: int = 0 # For reward classifier, collect more positive examples after a success to train a classifier +``` + + +### Finding Robot Workspace Bounds + +Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot. + +This helps simplify the problem of learning on the real robot in two ways: 1) by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration, and 2) by allowing training in end-effector space rather than joint space. Empirically, learning in joint space for reinforcement learning in manipulation is often a harder problem - some tasks are nearly impossible to learn in joint space but become learnable when the action space is transformed to end-effector coordinates. + +**Using find_joint_limits.py** + +This script helps you find the safe operational bounds for your robot's end-effector. Given that you have a follower and leader arm, you can use the script to find the bounds for the follower arm that will be applied during training. +Bounding the action space will reduce the redundant exploration of the agent and guarantees safety. + +```bash +python -m lerobot.scripts.find_joint_limits \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=black \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue +``` + +**Workflow** + +1. Run the script and move the robot through the space that solves the task +2. The script will record the minimum and maximum end-effector positions and the joint angles and prints them to the console, for example: + ``` + Max ee position [0.2417 0.2012 0.1027] + Min ee position [0.1663 -0.0823 0.0336] + Max joint positions [-20.0, -20.0, -20.0, -20.0, -20.0, -20.0] + Min joint positions [50.0, 50.0, 50.0, 50.0, 50.0, 50.0] + ``` +3. Use these values in the configuration of your teleoperation device (TeleoperatorConfig) under the `end_effector_bounds` field + +**Example Configuration** + +```json +"end_effector_bounds": { + "max": [0.24, 0.20, 0.10], + "min": [0.16, -0.08, 0.03] +} +``` + +### Collecting Demonstrations + +With the bounds defined, you can safely collect demonstrations for training. Training RL with off-policy algorithm allows us to use offline datasets collected in order to improve the efficiency of the learning process. + +**Setting Up Record Mode** + +Create a configuration file for recording demonstrations (or edit an existing one like [env_config_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)): + +1. Set `mode` to `"record"` +2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name") +3. Set `num_episodes` to the number of demonstrations you want to collect +4. Set `crop_params_dict` to `null` initially (we'll determine crops later) +5. Configure `robot`, `cameras`, and other hardware settings + +Example configuration section: +```json +"mode": "record", +"repo_id": "username/pick_lift_cube", +"dataset_root": null, +"task": "pick_and_lift", +"num_episodes": 15, +"episode": 0, +"push_to_hub": true +``` + +### Using a Teleoperation Device + +Along with your robot, you will need a teleoperation device to control it in order to collect datasets of your task and perform interventions during the online training. +We support using a gamepad or a keyboard or the leader arm of the robot. + +HIL-Serl learns actions in the end-effector space of the robot. Therefore, the teleoperation will control the end-effector's x,y,z displacements. + +For that we need to define a version of the robot that takes actions in the end-effector space. Check the robot class `SO100FollowerEndEffector` and its configuration `SO100FollowerEndEffectorConfig` for the default parameters related to the end-effector space. + +```python +class SO100FollowerEndEffectorConfig(SO100FollowerConfig): + """Configuration for the SO100FollowerEndEffector robot.""" + + # Default bounds for the end-effector position (in meters) + end_effector_bounds: dict[str, list[float]] = field( # bounds for the end-effector in x,y,z direction + default_factory=lambda: { + "min": [-1.0, -1.0, -1.0], # min x, y, z + "max": [1.0, 1.0, 1.0], # max x, y, z + } + ) + + max_gripper_pos: float = 50 # maximum gripper position that the gripper will be open at + + end_effector_step_sizes: dict[str, float] = field( # maximum step size for the end-effector in x,y,z direction + default_factory=lambda: { + "x": 0.02, + "y": 0.02, + "z": 0.02, + } + ) +``` + +The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/common/teleoperators`. + +**Setting up the Gamepad** + +The gamepad provides a very convenient way to control the robot and the episode state. + +To setup the gamepad, you need to set the `control_mode` to `"gamepad"` and define the `teleop` section in the configuration file. + +```json + "teleop": { + "type": "gamepad", + "use_gripper": true + }, +``` + +

+ Figure shows the control mappings on a Logitech gamepad. +

+

Gamepad button mapping for robot control and episode management

+ +**Setting up the SO101 leader** + +The SO101 leader arm has reduced gears that allows it to move and track the follower arm during exploration. Therefore, taking over is much smoother than the gearless SO100. + +To setup the SO101 leader, you need to set the `control_mode` to `"leader"` and define the `teleop` section in the configuration file. + +```json + "teleop": { + "type": "so101_leader", + "port": "/dev/tty.usbmodem585A0077921", # check your port number + "use_degrees": true + }, +``` + +In order to annotate the success/failure of the episode, **you will need** to use a keyboard to press `s` for success, `esc` for failure. +During the online training, press `space` to take over the policy and `space` again to give the control back to the policy. + +
+Video: SO101 leader teleoperation + +
+ +
+ +

SO101 leader teleoperation example, the leader tracks the follower, press `space` to intervene

+
+ +**Recording Demonstrations** + +Start the recording process, an example of the config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json): + +```bash +python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config_so100.json +``` + +During recording: +1. The robot will reset to the initial position defined in the configuration file `fixed_reset_joint_positions` +2. Complete the task successfully +3. The episode ends with a reward of 1 when you press the "success" button +4. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0 +5. You can rerecord an episode by pressing the "rerecord" button +6. The process automatically continues to the next episode +7. After recording all episodes, the dataset is pushed to the Hugging Face Hub (optional) and saved locally + + +### Processing the Dataset + +After collecting demonstrations, process them to determine optimal camera crops. +Reinforcement learning is sensitive to background distractions, so it is important to crop the images to the relevant workspace area. + +Visual RL algorithms learn directly from pixel inputs, making them vulnerable to irrelevant visual information. Background elements like changing lighting, shadows, people moving, or objects outside the workspace can confuse the learning process. Good ROI selection should: +- Include only the essential workspace where the task happens +- Capture the robot's end-effector and all objects involved in the task +- Exclude unnecessary background elements and distractions + +Note: If you already know the crop parameters, you can skip this step and just set the `crop_params_dict` in the configuration file during recording. + +**Determining Crop Parameters** + +Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images: + +```bash +python lerobot/scripts/rl/crop_dataset_roi.py --repo-id username/pick_lift_cube +``` + +1. For each camera view, the script will display the first frame +2. Draw a rectangle around the relevant workspace area +3. Press 'c' to confirm the selection +4. Repeat for all camera views +5. The script outputs cropping parameters and creates a new cropped dataset + +Example output: +``` +Selected Rectangular Regions of Interest (top, left, height, width): +observation.images.side: [180, 207, 180, 200] +observation.images.front: [180, 250, 120, 150] +``` + +

+ +

+ +

Interactive cropping tool for selecting regions of interest

+ + +**Updating Configuration** + +Add these crop parameters to your training configuration: + +```json +"crop_params_dict": { + "observation.images.side": [180, 207, 180, 200], + "observation.images.front": [180, 250, 120, 150] +}, +"resize_size": [128, 128] +``` + +**Recommended image resolution** + +Most vision-based policies have been validated on square inputs of either **128×128** (default) or **64×64** pixels. We therefore advise setting the resize_size parameter to [128, 128] – or [64, 64] if you need to save GPU memory and bandwidth. Other resolutions are possible but have not been extensively tested. + + +### Training a Reward Classifier + +The reward classifier plays an important role in the HIL-SERL workflow by automating reward assignment and automatically detecting episode success. Instead of manually defining reward functions or relying on human feedback for every timestep, the reward classifier learns to predict success/failure from visual observations. This enables the RL algorithm to learn efficiently by providing consistent and automated reward signals based on the robot's camera inputs. + +This guide explains how to train a reward classifier for human-in-the-loop reinforcement learning implementation of LeRobot. Reward classifiers learn to predict the reward value given a state which can be used in an RL setup to train a policy. + +**Note**: Training a reward classifier is optional. You can start the first round of RL experiments by annotating the success manually with your gamepad or keyboard device. + +The reward classifier implementation in `modeling_classifier.py` uses a pretrained vision model to process the images. It can output either a single value for binary rewards to predict success/fail cases or multiple values for multi-class settings. + +**Collecting a Dataset for the reward classifier** + +Before training, you need to collect a dataset with labeled examples. The `record_dataset` function in `gym_manipulator.py` enables the process of collecting a dataset of observations, actions, and rewards. + +To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig. + +```bash +python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/reward_classifier_train_config.json +``` + +**Key Parameters for Data Collection** + +- **mode**: set it to `"record"` to collect a dataset +- **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub +- **num_episodes**: Number of episodes to record +- **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected +- **fps**: Number of frames per second to record +- **push_to_hub**: Whether to push the dataset to the hub + +The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier. + +Example configuration section for data collection: + +```json +{ + "mode": "record", + "repo_id": "hf_username/dataset_name", + "dataset_root": "data/your_dataset", + "num_episodes": 20, + "push_to_hub": true, + "fps": 10, + "number_of_steps_after_success": 15 +} +``` + +**Reward Classifier Configuration** + +The reward classifier is configured using `configuration_classifier.py`. Here are the key parameters: + +- **model_name**: Base model architecture (e.g., we mainly use `"helper2424/resnet10"`) +- **model_type**: `"cnn"` or `"transformer"` +- **num_cameras**: Number of camera inputs +- **num_classes**: Number of output classes (typically 2 for binary success/failure) +- **hidden_dim**: Size of hidden representation +- **dropout_rate**: Regularization parameter +- **learning_rate**: Learning rate for optimizer + +Example configuration for training the [reward classifier](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/reward_classifier_train_config.json): + +```json +{ + "policy": { + "type": "reward_classifier", + "model_name": "helper2424/resnet10", + "model_type": "cnn", + "num_cameras": 2, + "num_classes": 2, + "hidden_dim": 256, + "dropout_rate": 0.1, + "learning_rate": 1e-4, + "device": "cuda", + "use_amp": true, + "input_features": { + "observation.images.front": { + "type": "VISUAL", + "shape": [3, 128, 128] + }, + "observation.images.side": { + "type": "VISUAL", + "shape": [3, 128, 128] + } + } + } +} +``` + +**Training the Classifier** + +To train the classifier, use the `train.py` script with your configuration: + +```bash +python lerobot/scripts/train.py --config_path path/to/reward_classifier_train_config.json +``` + +**Deploying and Testing the Model** + +To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to use your model: + +```python +env_config = HILSerlRobotEnvConfig( + reward_classifier_pretrained_path="path_to_your_pretrained_trained_model", + # Other environment parameters +) +``` +or set the argument in the json config file. + +```json +{ + "reward_classifier_pretrained_path": "path_to_your_pretrained_model" +} +``` + +Run `gym_manipulator.py` to test the model. +```bash +python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config.json +``` + +The reward classifier will automatically provide rewards based on the visual input from the robot's cameras. + +**Example Workflow for training the reward classifier** + +1. **Create the configuration files**: + Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/tree/main). + +2. **Collect a dataset**: + ```bash + python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json + ``` + +3. **Train the classifier**: + ```bash + python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json + ``` + +4. **Test the classifier**: + ```bash + python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json + ``` + +### Training with Actor-Learner + +The LeRobot system uses a distributed actor-learner architecture for training. This architecture decouples robot interactions from the learning process, allowing them to run concurrently without blocking each other. The actor server handles robot observations and actions, sending interaction data to the learner server. The learner server performs gradient descent and periodically updates the actor's policy weights. You will need to start two processes: a learner and an actor. + +**Configuration Setup** + +Create a training configuration file (example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_config_hilserl_so100.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`. + +1. Configure the policy settings (`type="sac"`, `device`, etc.) +2. Set `dataset` to your cropped dataset +3. Configure environment settings with crop parameters +4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/common/policies/sac/configuration_sac.py#L79). +5. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task. + +**Starting the Learner** + +First, start the learner server process: + +```bash +python lerobot/scripts/rl/learner.py --config_path lerobot/configs/train_config_hilserl_so100.json +``` + +The learner: +- Initializes the policy network +- Prepares replay buffers +- Opens a `gRPC` server to communicate with actors +- Processes transitions and updates the policy + +**Starting the Actor** + +In a separate terminal, start the actor process with the same configuration: + +```bash +python lerobot/scripts/rl/actor.py --config_path lerobot/configs/train_config_hilserl_so100.json +``` + +The actor: +- Connects to the learner via `gRPC` +- Initializes the environment +- Execute rollouts of the policy to collect experience +- Sends transitions to the learner +- Receives updated policy parameters + +**Training Flow** + +The training proceeds automatically: + +1. The actor executes the policy in the environment +2. Transitions are collected and sent to the learner +3. The learner updates the policy based on these transitions +4. Updated policy parameters are sent back to the actor +5. The process continues until the specified step limit is reached + +**Human in the Loop** + +- The key to learning efficiently is to have human interventions to provide corrective feedback and completing the task to aide the policy learning and exploration. +- To perform human interventions, you can press the upper right trigger button on the gamepad (or the `space` key on the keyboard). This will pause the policy actions and allow you to take over. +- A successful experiment is one where the human has to intervene at the start but then reduces the amount of interventions as the policy improves. You can monitor the intervention rate in the `wandb` dashboard. + +

+ Figure shows the control mappings on a Logitech gamepad. +

+ +

Example showing how human interventions help guide policy learning over time

+ +- The figure shows the plot of the episodic reward over interaction step. The figure shows the effect of human interventions on the policy learning. +- The orange curve is an experiment without any human interventions. While the pink and blue curves are experiments with human interventions. +- We can observe that the number of steps where the policy starts achieving the maximum reward is cut by a quarter when human interventions are present. + +**Monitoring and Debugging** + +If you have `wandb.enable` set to `true` in your configuration, you can monitor training progress in real-time through the [Weights & Biases](https://wandb.ai/site/) dashboard. + +### Guide to Human Interventions +The learning process is very sensitive to the intervention strategy. It will takes a few runs to understand how to intervene effectively. Some tips and hints: +- Allow the policy to explore for a few episodes at the start of training. +- Avoid intervening for long periods of time. Try to intervene in situation to correct the robot's behaviour when it goes off track. +- Once the policy starts achieving the task, even if its not perfect, you can limit your interventions to simple quick actions like a simple grasping commands. + +The ideal behaviour is that your intervention rate should drop gradually during training as shown in the figure below. + +

+ Intervention rate +

+ +

Plot of the intervention rate during a training run on a pick and lift cube task

+ +### Key hyperparameters to tune + +Some configuration values have a disproportionate impact on training stability and speed: + +- **`temperature_init`** (`policy.temperature_init`) – initial entropy temperature in SAC. Higher values encourage more exploration; lower values make the policy more deterministic early on. A good starting point is `1e-2`. We observed that setting it too high can make human interventions ineffective and slow down learning. +- **`policy_parameters_push_frequency`** (`policy.actor_learner_config.policy_parameters_push_frequency`) – interval in *seconds* between two weight pushes from the learner to the actor. The default is `4 s`. Decrease to **1-2 s** to provide fresher weights (at the cost of more network traffic); increase only if your connection is slow, as this will reduce sample efficiency. +- **`storage_device`** (`policy.storage_device`) – device on which the learner keeps the policy parameters. If you have spare GPU memory, set this to `"cuda"` (instead of the default `"cpu"`). Keeping the weights on-GPU removes CPU→GPU transfer overhead and can significantly increase the number of learner updates per second. + + +Congrats 🎉, you have finished this tutorial! + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). + +Paper citation: +``` +@article{luo2024precise, + title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}, + author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey}, + journal={arXiv preprint arXiv:2410.21845}, + year={2024} +} +``` diff --git a/docs/source/hilserl_sim.mdx b/docs/source/hilserl_sim.mdx new file mode 100644 index 0000000000000000000000000000000000000000..4832d1393620878fb9fde9812d69beff1dcfea88 --- /dev/null +++ b/docs/source/hilserl_sim.mdx @@ -0,0 +1,120 @@ +# Train RL in Simulation + +This guide explains how to use the `gym_hil` simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning. + +`gym_hil` is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to: + +- Train policies in simulation to test the RL stack before training on real robots + +- Collect demonstrations in sim using external devices like gamepads or keyboards +- Perform human interventions during policy learning + +Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube. + + +## Installation + +First, install the `gym_hil` package within the LeRobot environment: + +```bash +pip install -e ".[hilserl]" +``` + +## What do I need? + +- A gamepad or keyboard to control the robot +- A Nvidia GPU + + + +## Configuration + +To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include: + +### Environment Type and Task + +```json +{ + "type": "hil", + "name": "franka_sim", + "task": "PandaPickCubeGamepad-v0", + "device": "cuda" +} +``` + +Available tasks: +- `PandaPickCubeBase-v0`: Basic environment +- `PandaPickCubeGamepad-v0`: With gamepad control +- `PandaPickCubeKeyboard-v0`: With keyboard control + +### Gym Wrappers Configuration + +```json +"wrapper": { + "gripper_penalty": -0.02, + "control_time_s": 15.0, + "use_gripper": true, + "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785], + "end_effector_step_sizes": { + "x": 0.025, + "y": 0.025, + "z": 0.025 + }, + "control_mode": "gamepad" + } +``` + +Important parameters: +- `gripper_penalty`: Penalty for excessive gripper movement +- `use_gripper`: Whether to enable gripper control +- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector +- `control_mode`: Set to `"gamepad"` to use a gamepad controller + +## Running with HIL RL of LeRobot + +### Basic Usage + +To run the environment, set mode to null: + +```python +python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json +``` + +### Recording a Dataset + +To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record: + +```python +python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json +``` + +### Training a Policy + +To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers: + +```python +python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json +``` + +In a different terminal, run the learner server: + +```python +python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json +``` + +The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots. + +Congrats 🎉, you have finished this tutorial! + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). + +Paper citation: +``` +@article{luo2024precise, + title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}, + author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey}, + journal={arXiv preprint arXiv:2410.21845}, + year={2024} +} +``` diff --git a/docs/source/il_robots.mdx b/docs/source/il_robots.mdx new file mode 100644 index 0000000000000000000000000000000000000000..9491a93e85c4163834c570f16ea10ecac5192759 --- /dev/null +++ b/docs/source/il_robots.mdx @@ -0,0 +1,311 @@ +# Imitation Learning on Real-World Robots + +This tutorial will explain how to train a neural network to control a real robot autonomously. + +**You'll learn:** +1. How to record and visualize your dataset. +2. How to train a policy using your data and prepare it for evaluation. +3. How to evaluate your policy and visualize the results. + +By following these steps, you'll be able to replicate tasks, such as picking up a Lego block and placing it in a bin with a high success rate, as shown in the video below. + +
+Video: pickup lego block task + +
+ +
+ +
+ +This tutorial isn’t tied to a specific robot: we walk you through the commands and API snippets you can adapt for any supported platform. + +During data collection, you’ll use a “teloperation” device, such as a leader arm or keyboard to teleoperate the robot and record its motion trajectories. + +Once you’ve gathered enough trajectories, you’ll train a neural network to imitate these trajectories and deploy the trained model so your robot can perform the task autonomously. + +If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support. + +## Set up and Calibrate + +If you haven't yet set up and calibrated your robot and teleop device, please do so by following the robot-specific tutorial. + +## Teleoperate + +In this example, we’ll demonstrate how to teleoperate the SO101 robot. For each command, we also provide a corresponding API example. + +Note that the `id` associated with a robot is used to store the calibration file. It's important to use the same `id` when teleoperating, recording, and evaluating when using the same setup. + + + +```bash +python -m lerobot.teleoperate \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=my_awesome_follower_arm \ + --teleop.type=so101_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=my_awesome_leader_arm +``` + + +```python +from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader +from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower + +robot_config = SO101FollowerConfig( + port="/dev/tty.usbmodem58760431541", + id="my_red_robot_arm", +) + +teleop_config = SO101LeaderConfig( + port="/dev/tty.usbmodem58760431551", + id="my_blue_leader_arm", +) + +robot = SO101Follower(robot_config) +teleop_device = SO101Leader(teleop_config) +robot.connect() +teleop_device.connect() + +while True: + action = teleop_device.get_action() + robot.send_action(action) +``` + + + +The teleoperate command will automatically: +1. Identify any missing calibrations and initiate the calibration procedure. +2. Connect the robot and teleop device and start teleoperation. + +## Cameras + +To add cameras to your setup, follow this [Guide](./cameras#setup-cameras). + +## Teleoperate with cameras + +With `rerun`, you can teleoperate again while simultaneously visualizing the camera feeds and joint positions. In this example, we’re using the Koch arm. + + + +```bash +python -m lerobot.teleoperate \ + --robot.type=koch_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=my_awesome_follower_arm \ + --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ + --teleop.type=koch_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=my_awesome_leader_arm \ + --display_data=true +``` + + +```python +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader +from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower + +camera_config = { + "front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30) +} + +robot_config = KochFollowerConfig( + port="/dev/tty.usbmodem585A0076841", + id="my_red_robot_arm", + cameras=camera_config +) + +teleop_config = KochLeaderConfig( + port="/dev/tty.usbmodem58760431551", + id="my_blue_leader_arm", +) + +robot = KochFollower(robot_config) +teleop_device = KochLeader(teleop_config) +robot.connect() +teleop_device.connect() + +while True: + observation = robot.get_observation() + action = teleop_device.get_action() + robot.send_action(action) +``` + + + +## Record a dataset + +Once you're familiar with teleoperation, you can record your first dataset. + +We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens). + +Add your token to the CLI by running this command: +```bash +huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential +``` + +Then store your Hugging Face repository name in a variable: +```bash +HF_USER=$(huggingface-cli whoami | head -n 1) +echo $HF_USER +``` + +Now you can record a dataset. To record 2 episodes and upload your dataset to the hub, execute this command tailored to the SO101. +```bash +python -m lerobot.record \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem585A0076841 \ + --robot.id=my_awesome_follower_arm \ + --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ + --teleop.type=so101_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=my_awesome_leader_arm \ + --display_data=true \ + --dataset.repo_id=${HF_USER}/record-test \ + --dataset.num_episodes=2 \ + --dataset.single_task="Grab the black cube" +``` + +#### Dataset upload +Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running: +```bash +echo https://huggingface.co/datasets/${HF_USER}/so101_test +``` +Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example). + +You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot). + +#### Record function + +The `record` function provides a suite of tools for capturing and managing data during robot operation: + +##### 1. Data Storage +- Data is stored using the `LeRobotDataset` format and is stored on disk during recording. +- By default, the dataset is pushed to your Hugging Face page after recording. + - To disable uploading, use `--dataset.push_to_hub=False`. + +##### 2. Checkpointing and Resuming +- Checkpoints are automatically created during recording. +- If an issue occurs, you can resume by re-running the same command with `--control.resume=true`. +- To start recording from scratch, **manually delete** the dataset directory. + +##### 3. Recording Parameters +Set the flow of data recording using command-line arguments: +- `--dataset.episode_time_s=60` + Duration of each data recording episode (default: **60 seconds**). +- `--dataset.reset_time_s=60` + Duration for resetting the environment after each episode (default: **60 seconds**). +- `--dataset.num_episodes=50` + Total number of episodes to record (default: **50**). + +##### 4. Keyboard Controls During Recording +Control the data recording flow using keyboard shortcuts: +- Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next. +- Press **Left Arrow (`←`)**: Cancel the current episode and re-record it. +- Press **Escape (`ESC`)**: Immediately stop the session, encode videos, and upload the dataset. + +#### Tips for gathering data + +Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images. + +In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions. + +Avoid adding too much variation too quickly, as it may hinder your results. + +If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset. + + +#### Troubleshooting: +- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux). + +## Visualize a dataset + +If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by: +```bash +echo ${HF_USER}/so101_test +``` + +## Replay an episode + +A useful feature is the `replay` function, which allows you to replay any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model. + +You can replay the first episode on your robot with: +```bash +python -m lerobot.replay \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=my_awesome_follower_arm \ + --dataset.repo_id=${HF_USER}/record-test \ + --dataset.episode=0 # choose the episode you want to replay +``` + +Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com). + +## Train a policy + +To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command: +```bash +python lerobot/scripts/train.py \ + --dataset.repo_id=${HF_USER}/so101_test \ + --policy.type=act \ + --output_dir=outputs/train/act_so101_test \ + --job_name=act_so101_test \ + --policy.device=cuda \ + --wandb.enable=true +``` + +Let's explain the command: +1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_test`. +2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset. +4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon. +5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`. + +Training should take several hours. You will find checkpoints in `outputs/train/act_so101_test/checkpoints`. + +To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy: +```bash +python lerobot/scripts/train.py \ + --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \ + --resume=true +``` + +#### Train using Collab +If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act). + +#### Upload policy checkpoints + +Once training is done, upload the latest checkpoint with: +```bash +huggingface-cli upload ${HF_USER}/act_so101_test \ + outputs/train/act_so101_test/checkpoints/last/pretrained_model +``` + +You can also upload intermediate checkpoints with: +```bash +CKPT=010000 +huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \ + outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model +``` + +## Evaluate your policy + +You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes: +```bash +python -m lerobot.record \ + --robot.type=so100_follower \ + --robot.port=/dev/ttyACM1 \ + --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \ + --robot.id=my_awesome_follower_arm \ + --display_data=false \ + --dataset.repo_id=$HF_USER/eval_so100 \ + --dataset.single_task="Put lego brick into the transparent box" \ + --policy.path=${HF_USER}/my_policy +``` + +As you can see, it's almost the same command as previously used to record your training dataset. Two things changed: +1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`). +2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`). diff --git a/docs/source/il_sim.mdx b/docs/source/il_sim.mdx new file mode 100644 index 0000000000000000000000000000000000000000..66793c142f355e3d4e0313064edf3b3ce4f647a7 --- /dev/null +++ b/docs/source/il_sim.mdx @@ -0,0 +1,152 @@ +# Imitation Learning in Sim + +This tutorial will explain how to train a neural network to control a robot in simulation with imitation learning. + +**You'll learn:** +1. How to record a dataset in simulation with [gym-hil](https://github.com/huggingface/gym-hil) and visualize the dataset. +2. How to train a policy using your data. +3. How to evaluate your policy in simulation and visualize the results. + +For the simulation environment we use the same [repo](https://github.com/huggingface/gym-hil) that is also being used by the Human-In-the-Loop (HIL) reinforcement learning algorithm. +This environment is based on [MuJoCo](https://mujoco.org) and allows you to record datasets in LeRobotDataset format. +Teleoperation is easiest with a controller like the Logitech F710, but you can also use your keyboard if you are up for the challenge. + +## Installation + +First, install the `gym_hil` package within the LeRobot environment, go to your LeRobot folder and run this command: + +```bash +pip install -e ".[hilserl]" +``` + +## Teleoperate and Record a Dataset + +To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_gym_hil_il.json). + +To teleoperate and collect a dataset, we need to modify this config file and you should add your `repo_id` here: `"repo_id": "il_gym",` and `"num_episodes": 30,` and make sure you set `mode` to `record`, "mode": "record". + +If you do not have a Nvidia GPU also change `"device": "cuda"` parameter in the config file (for example to `mps` for MacOS). + +By default the config file assumes you use a controller. To use your keyboard please change the envoirment specified at `"task"` in the config file and set it to `"PandaPickCubeKeyboard-v0"`. + +Then we can run this command to start: + + + + +```bash +python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config_gym_hil_il.json +``` + + + + +```bash +mjpython lerobot/scripts/rl/gym_manipulator.py --config_path path/to/env_config_gym_hil_il.json +``` + + + + +Once rendered you can teleoperate the robot with the gamepad or keyboard, below you can find the gamepad/keyboard controls. + +Note that to teleoperate the robot you have to hold the "Human Take Over Pause Policy" Button `RB` to enable control! + +**Gamepad Controls** + +

+ Figure shows the control mappings on a Logitech gamepad. +

+

Gamepad button mapping for robot control and episode management

+ +**Keyboard controls** + +For keyboard controls use the `spacebar` to enable control and the following keys to move the robot: +```bash + Arrow keys: Move in X-Y plane + Shift and Shift_R: Move in Z axis + Right Ctrl and Left Ctrl: Open and close gripper + ESC: Exit +``` + +## Visualize a dataset + +If you uploaded your dataset to the hub you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id. + +

+ Figure shows the dataset visualizer +

+

Dataset visualizer

+ + +## Train a policy + +To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command: +```bash +python lerobot/scripts/train.py \ + --dataset.repo_id=${HF_USER}/il_gym \ + --policy.type=act \ + --output_dir=outputs/train/il_sim_test \ + --job_name=il_sim_test \ + --policy.device=cuda \ + --wandb.enable=true +``` + +Let's explain the command: +1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`. +2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset. +4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon. +5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`. + +Training should take several hours, 100k steps (which is the default) will take about 1h on Nvidia A100. You will find checkpoints in `outputs/train/il_sim_test/checkpoints`. + +#### Train using Collab +If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act). + +#### Upload policy checkpoints + +Once training is done, upload the latest checkpoint with: +```bash +huggingface-cli upload ${HF_USER}/il_sim_test \ + outputs/train/il_sim_test/checkpoints/last/pretrained_model +``` + +You can also upload intermediate checkpoints with: +```bash +CKPT=010000 +huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \ + outputs/train/il_sim_test/checkpoints/${CKPT}/pretrained_model +``` + +## Evaluate your policy in Sim + +To evaluate your policy we have to use the config file that can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json). + +Make sure to replace the `repo_id` with the dataset you trained on, for example `pepijn223/il_sim_dataset` and replace the `pretrained_policy_name_or_path` with your model id, for example `pepijn223/il_sim_model` + +Then you can run this command to visualize your trained policy + + + + +```bash +python lerobot/scripts/rl/eval_policy.py --config_path=path/to/eval_config_gym_hil.json +``` + + + + +```bash +mjpython lerobot/scripts/rl/eval_policy.py --config_path=path/to/eval_config_gym_hil.json +``` + + + + +> [!WARNING] +> While the main workflow of training ACT in simulation is straightforward, there is significant room for exploring how to set up the task, define the initial state of the environment, and determine the type of data required during collection to learn the most effective policy. If your trained policy doesn't perform well, investigate the quality of the dataset it was trained on using our visualizers, as well as the action values and various hyperparameters related to ACT and the simulation. + +Congrats 🎉, you have finished this tutorial. If you want to continue with using LeRobot in simulation follow this [Tutorial on reinforcement learning in sim with HIL-SERL](https://huggingface.co/docs/lerobot/hilserl_sim) + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/docs/source/index.mdx b/docs/source/index.mdx new file mode 100644 index 0000000000000000000000000000000000000000..d578ec80fe2c5781555c3d9a57dca2c855b58db9 --- /dev/null +++ b/docs/source/index.mdx @@ -0,0 +1,19 @@ +
+ + HuggingFace Expert Acceleration Program + +
+ +# LeRobot + +**State-of-the-art machine learning for real-world robotics** + +🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models. + +🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning. + +🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. + +🤗 LeRobot hosts pretrained models and datasets on the LeRobot HuggingFace page. + +Join the LeRobot community on [Discord](https://discord.gg/s3KuuzsPFb) diff --git a/docs/source/installation.mdx b/docs/source/installation.mdx new file mode 100644 index 0000000000000000000000000000000000000000..381cf78d57264ae539d0e8c51f5cf1292ad070da --- /dev/null +++ b/docs/source/installation.mdx @@ -0,0 +1,72 @@ +# Installation + +## Install LeRobot + +Currently only available from source. + +Download our source code: +```bash +git clone https://github.com/huggingface/lerobot.git +cd lerobot +``` + +Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) +```bash +conda create -y -n lerobot python=3.10 +``` + +Then activate your conda environment, you have to do this each time you open a shell to use lerobot: +```bash +conda activate lerobot +``` + +When using `miniconda`, install `ffmpeg` in your environment: +```bash +conda install ffmpeg -c conda-forge +``` + +> [!TIP] +> This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can: +> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using: +> ```bash +> conda install ffmpeg=7.1.1 -c conda-forge +> ``` +> - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`. + +Install 🤗 LeRobot: +```bash +pip install -e . +``` + +### Troubleshooting +If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`. +To install these for linux run: +```bash +sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config +``` +For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg) + +## Optional dependencies + +LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`. + +### Simulations +Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht)) +Example: +```bash +pip install -e ".[aloha]" # or "[pusht]" for example +``` + +### Motor Control +For Koch v1.1 install the Dynamixel SDK, for SO100/SO101/Moss install the Feetech SDK. +```bash +pip install -e ".[feetech]" # or "[dynamixel]" for example +``` + +### Experiment Tracking +To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with +```bash +wandb login +``` + +You can now assemble your robot if it's not ready yet, look for your robot type on the left. Then follow the link below to use Lerobot with your robot. diff --git a/docs/source/integrate_hardware.mdx b/docs/source/integrate_hardware.mdx new file mode 100644 index 0000000000000000000000000000000000000000..305c5b65c2a7923f22b4f024cf09b6ed290a4852 --- /dev/null +++ b/docs/source/integrate_hardware.mdx @@ -0,0 +1,318 @@ +# Bring Your Own Hardware + +This tutorial will explain how to integrate your own robot design into the LeRobot ecosystem and have it access all of our tools (data collection, control pipelines, policy training and inference). + +To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robots/robot.py) base class in the LeRobot which specifies a standard interface for physical robot integration. Let's see how to implement it. + +## Prerequisites + +- Your own robot which exposes a communication interface (e.g. serial, CAN, TCP) +- A way to read sensor data and send motor commands programmatically, e.g. manufacturer's SDK or API, or your own protocol implementation. +- LeRobot installed in your environment. Follow our [Installation Guide](./installation). + +## Choose your motors + +If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus interfaces: + +- [`FeetechMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/feetech/feetech.py) – for controlling Feetech servos +- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos + +Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/motors_bus.py) abstract class to learn about its API. +For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robots/so101_follower/so101_follower.py) + +Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial): +- Find an existing SDK in Python (or use bindings to C/C++) +- Or implement a basic communication wrapper (e.g., via pyserial, socket, or CANopen) + +You're not alone—many community contributions use custom boards or firmware! + +For Feetech and Dynamixel, we currently support these servos: + - Feetech: + - STS & SMS series (protocol 0): `sts3215`, `sts3250`, `sm8512bl` + - SCS series (protocol 1): `scs0009` + - Dynamixel (protocol 2.0 only): `xl330-m077`, `xl330-m288`, `xl430-w250`, `xm430-w350`, `xm540-w270`, `xc430-w150` + +If you are using Feetech or Dynamixel servos that are not in this list, you can add those in the [Feetech table](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/feetech/tables.py) or [Dynamixel table](https://github.com/huggingface/lerobot/blob/main/lerobot/common/motors/dynamixel/tables.py). Depending on the model, this will require you to add model-specific information. In most cases though, there shouldn't be a lot of additions to do. + +In the next sections, we'll use a `FeetechMotorsBus` as the motors interface for the examples. Replace it and adapt to your motors if necessary. + +## Step 1: Subclass the `Robot` Interface + +You’ll first need to specify the config class and a string identifier (`name`) for your robot. If your robot has special needs that you'd like to be able to change easily, it should go here (e.g. port/address, baudrate). + +Here, we'll add the port name and one camera by default for our robot: +```python +from dataclasses import dataclass, field + +from lerobot.common.cameras import CameraConfig +from lerobot.common.cameras.opencv import OpenCVCameraConfig +from lerobot.common.robots import RobotConfig + + +@RobotConfig.register_subclass("my_cool_robot") +@dataclass +class MyCoolRobotConfig(RobotConfig): + port: str + cameras: dict[str, CameraConfig] = field( + default_factory={ + "cam_1": OpenCVCameraConfig( + index_or_path=2, + fps=30, + width=480, + height=640, + ), + } + ) +``` + +Have a look at our [Cameras tutorial](./cameras) to understand how to detect and add your camera. + +Next, we'll create our actual robot class which inherits from `Robot`. This abstract class defines a contract you must follow for your robot to be usable with the rest of the LeRobot tools. + +Here we'll create a simple 5-DoF robot with one camera. It could be a simple arm but notice that the `Robot` abstract class does not assume anything on your robot's form factor. You can let you imagination run wild when designing new robots! + +```python +from lerobot.common.cameras import make_cameras_from_configs +from lerobot.common.motors import Motor, MotorNormMode +from lerobot.common.motors.feetech import FeetechMotorsBus +from lerobot.common.robots import Robot + +class MyCoolRobot(Robot): + config_class = MyCoolRobotConfig + name = "my_cool_robot" + + def __init__(self, config: MyCoolRobotConfig): + super().__init__(config) + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + "joint_1": Motor(1, "sts3250", MotorNormMode.RANGE_M100_100), + "joint_2": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100), + "joint_3": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100), + "joint_4": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100), + "joint_5": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100), + }, + calibration=self.calibration, + ) + self.cameras = make_cameras_from_configs(config.cameras) +``` + +## Step 2: Define Observation and Action Features + +These two properties define the *interface contract* between your robot and tools that consume it (such as data collection or learning pipelines). + +> [!WARNING] +> Note that these properties must be callable even if the robot is not yet connected, so avoid relying on runtime hardware state to define them. + +### `observation_features` + +This property should return a dictionary describing the structure of sensor outputs from your robot. The keys match what `get_observation()` returns, and the values describe either the shape (for arrays/images) or the type (for simple values). + +Example for our 5-DoF arm with one camera: +```python +@property +def _motors_ft(self) -> dict[str, type]: + return { + "joint_1.pos": float, + "joint_2.pos": float, + "joint_3.pos": float, + "joint_4.pos": float, + "joint_5.pos": float, + } + +@property +def _cameras_ft(self) -> dict[str, tuple]: + return { + cam: (self.cameras[cam].height, self.cameras[cam].width, 3) for cam in self.cameras + } + +@property +def observation_features(self) -> dict: + return {**self._motors_ft, **self._cameras_ft} +``` +In this case, observations consist of a simple dict storing each motor's position and a camera image. + +### `action_features` + +This property describes the commands your robot expects via `send_action()`. Again, keys must match the expected input format, and values define the shape/type of each command. + +Here, we simply use the same joints proprioceptive features (`self._motors_ft`) as with `observation_features`: the action sent will simply the goal position for each motor. +```python +def action_features(self) -> dict: + return self._motors_ft +``` + +## Step 3: Handle Connection and Disconnection + +These methods should handle opening and closing communication with your hardware (e.g. serial ports, CAN interfaces, USB devices, cameras). + +### `is_connected` + +This property should simply reflect that communication with the robot's hardware is established. When this property is `True`, it should be possible to read and write to the hardware using `get_observation()` and `send_action()`. + +```python +@property +def is_connected(self) -> bool: + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) +``` + +### `connect()` + +This method should establish communication with the hardware. Moreover, if your robot needs calibration and is not calibrated, it should start a calibration procedure by default. If your robot needs some specific configuration, this should also be called here. + +```python +def connect(self, calibrate: bool = True) -> None: + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() +``` + +### `disconnect()` + +This method should gracefully terminate communication with the hardware: free any related resources (threads or processes), close ports, etc. + +Here, we already handle this in our `MotorsBus` and `Camera` classes so we just need to call their own `disconnect()` methods: +```python +def disconnect(self) -> None: + self.bus.disconnect() + for cam in self.cameras.values(): + cam.disconnect() +``` + +## Step 4: Support Calibration and Configuration + +LeRobot supports saving and loading calibration data automatically. This is useful for joint offsets, zero positions, or sensor alignment. + +> Note that depending on your hardware, this may not apply. If that's the case, you can simply leave these methods as no-ops: +> ```python +> @property +> def is_calibrated(self) -> bool: +> return True +> +> def calibrate(self) -> None: +> pass +> ``` + +### `is_calibrated` + +This should reflect whether your robot has the required calibration loaded. + +```python +@property +def is_calibrated(self) -> bool: + return self.bus.is_calibrated +``` + +### `calibrate()` + +The goal of the calibration is twofold: + - Know the physical range of motion of each motors in order to only send commands within this range. + - Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere. + +It should implement the logic for calibration (if relevant) and update the `self.calibration` dictionary. If you are using Feetech or Dynamixel motors, our bus interfaces already include methods to help with this. + +```python +def calibrate(self) -> None: + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + print( + "Move all joints sequentially through their entire ranges " + "of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion() + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print("Calibration saved to", self.calibration_fpath) +``` + +### `configure()` + +Use this to set up any configuration for your hardware (servos control modes, controller gains, etc.). This should usually be run at connection time and be idempotent. + +```python +def configure(self) -> None: + with self.bus.torque_disabled(): + self.bus.configure_motors() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + self.bus.write("P_Coefficient", motor, 16) + self.bus.write("I_Coefficient", motor, 0) + self.bus.write("D_Coefficient", motor, 32) +``` + +## Step 5: Implement Sensors Reading and Action Sending + +These are the most important runtime functions: the core I/O loop. + +### `get_observation()` + +Returns a dictionary of sensor values from the robot. These typically include motor states, camera frames, various sensors, etc. In the LeRobot framework, these observations are what will be fed to a policy in order to predict the actions to take. The dictionary keys and structure must match `observation_features`. + +```python +def get_observation(self) -> dict[str, Any]: + if not self.is_connected: + raise ConnectionError(f"{self} is not connected.") + + # Read arm position + obs_dict = self.bus.sync_read("Present_Position") + obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + obs_dict[cam_key] = cam.async_read() + + return obs_dict +``` + +### `send_action()` + +Takes a dictionary that matches `action_features`, and sends it to your hardware. You can add safety limits (clipping, smoothing) and return what was actually sent. + +For simplicity, we won't be adding any modification of the actions in our example here. + +```python +def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + goal_pos = {key.removesuffix(".pos"): val for key, val in action.items()} + + # Send goal position to the arm + self.bus.sync_write("Goal_Position", goal_pos) + + return action +``` + +## Adding a Teleoperator + +For implementing teleoperation devices, we also provide a [`Teleoperator`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/teleoperators/teleoperator.py) base class. This class is very similar to the `Robot` base class and also doesn't assume anything on form factor. + +The main differences are in the I/O functions: a teleoperator allows you to produce action via `get_action` and can receive feedback actions via `send_feedback`. Feedback could be anything controllable on the teleoperation device that could help the person controlling it understand the consequences of the actions sent. Think motion/force feedback on a leader arm, vibrations on a gamepad controller for example. To implement a teleoperator, you can follow this same tutorial and adapt it for these two methods. + +## Wrapping Up + +Once your robot class is complete, you can leverage the LeRobot ecosystem: + +- Control your robot with available teleoperators or integrate directly your teleoperating device +- Record training data and visualize it +- Integrate it into RL or imitation learning pipelines + +Don't hesitate to reach out to the community for help on our [Discord](https://discord.gg/s3KuuzsPFb) 🤗 diff --git a/docs/source/koch.mdx b/docs/source/koch.mdx new file mode 100644 index 0000000000000000000000000000000000000000..b2399ae62e0693afd9bb9295e1ecd85a266ce95e --- /dev/null +++ b/docs/source/koch.mdx @@ -0,0 +1 @@ +../../lerobot/common/robots/koch_follower/koch.mdx \ No newline at end of file diff --git a/docs/source/lekiwi.mdx b/docs/source/lekiwi.mdx new file mode 100644 index 0000000000000000000000000000000000000000..e2b4ff55263ec8199c63ac44ca3f0174b815cb60 --- /dev/null +++ b/docs/source/lekiwi.mdx @@ -0,0 +1 @@ +../../lerobot/common/robots/lekiwi/lekiwi.mdx \ No newline at end of file diff --git a/docs/source/notebooks.mdx b/docs/source/notebooks.mdx new file mode 100644 index 0000000000000000000000000000000000000000..f935deb4e83469f91807e390faead59c083085f4 --- /dev/null +++ b/docs/source/notebooks.mdx @@ -0,0 +1,29 @@ +# 🤗 LeRobot Notebooks + +This repository contains example notebooks for using LeRobot. These notebooks demonstrate how to train policies on real or simulation datasets using standardized policies. + +--- + +### Training ACT + +[ACT](https://huggingface.co/papers/2304.13705) (Action Chunking Transformer) is a transformer-based policy architecture for imitation learning that processes robot states and camera inputs to generate smooth, chunked action sequences. + +We provide a ready-to-run Google Colab notebook to help you train ACT policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases. + +| Notebook | Colab | +|:---------|:------| +| [Train ACT with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) | + +Expected training time for 100k steps: ~1.5 hours on an NVIDIA A100 GPU with batch size of `64`. + +### Training SmolVLA + +[SmolVLA](https://huggingface.co/papers/2506.01844) is a small but efficient Vision-Language-Action model. It is compact in size with 450 M-parameter and is developed by Hugging Face. + +We provide a ready-to-run Google Colab notebook to help you train SmolVLA policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases. + +| Notebook | Colab | +| :-------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [Train SmolVLA with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) | + +Expected training time for 20k steps: ~5 hours on an NVIDIA A100 GPU with batch size of `64`. diff --git a/docs/source/smolvla.mdx b/docs/source/smolvla.mdx new file mode 100644 index 0000000000000000000000000000000000000000..05c5846ac9ab9aedd6493b26efe8986f7e229c57 --- /dev/null +++ b/docs/source/smolvla.mdx @@ -0,0 +1,93 @@ +# Finetune SmolVLA + +SmolVLA is Hugging Face’s lightweight foundation model for robotics. Designed for easy fine-tuning on LeRobot datasets, it helps accelerate your development! + +

+ SmolVLA architecture. +
+ Figure 1. SmolVLA takes as input (i) multiple cameras views, (ii) the robot’s current sensorimotor state, and (iii) a natural language instruction, encoded into contextual features used to condition the action expert when generating an action chunk. +

+ +## Set Up Your Environment + +1. Install LeRobot by following our [Installation Guide](./installation). +2. Install SmolVLA dependencies by running: + + ```bash + pip install -e ".[smolvla]" + ``` + +## Collect a dataset + +SmolVLA is a base model, so fine-tuning on your own data is required for optimal performance in your setup. +We recommend recording ~50 episodes of your task as a starting point. Follow our guide to get started: [Recording a Dataset](https://huggingface.co/docs/lerobot/getting_started_real_world_robot#record-a-dataset) + + + +In your dataset, make sure to have enough demonstrations per each variation (e.g. the cube position on the table if it is cube pick-place task) you are introducing. + +We recommend checking out the dataset linked below for reference that was used in the [SmolVLA paper](https://huggingface.co/papers/2506.01844): + +🔗 [SVLA SO100 PickPlace](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2Flerobot%2Fsvla_so100_pickplace%2Fepisode_0) + +In this dataset, we recorded 50 episodes across 5 distinct cube positions. For each position, we collected 10 episodes of pick-and-place interactions. This structure, repeating each variation several times, helped the model generalize better. We tried similar dataset with 25 episodes, and it was not enough leading to a bad performance. So, the data quality and quantity is definitely a key. +After you have your dataset available on the Hub, you are good to go to use our finetuning script to adapt SmolVLA to your application. + + +## Finetune SmolVLA on your data + +Use [`smolvla_base`](https://hf.co/lerobot/smolvla_base), our pretrained 450M model, and fine-tune it on your data. +Training the model for 20k steps will roughly take ~4 hrs on a single A100 GPU. You should tune the number of steps based on performance and your use-case. + +If you don't have a gpu device, you can train using our notebook on [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) + +Pass your dataset to the training script using `--dataset.repo_id`. If you want to test your installation, run the following command where we use one of the datasets we collected for the [SmolVLA Paper](https://huggingface.co/papers/2506.01844). + +```bash +cd lerobot && python lerobot/scripts/train.py \ + --policy.path=lerobot/smolvla_base \ + --dataset.repo_id=${HF_USER}/mydataset \ + --batch_size=64 \ + --steps=20000 \ + --output_dir=outputs/train/my_smolvla \ + --job_name=my_smolvla_training \ + --policy.device=cuda \ + --wandb.enable=true +``` + + +You can start with a small batch size and increase it incrementally, if the GPU allows it, as long as loading times remain short. + + +Fine-tuning is an art. For a complete overview of the options for finetuning, run + +```bash +python lerobot/scripts/train.py --help +``` + +

+ Comparison of SmolVLA across task variations. +
+ Figure 2: Comparison of SmolVLA across task variations. From left to right: (1) pick-place cube counting, (2) pick-place cube counting, (3) pick-place cube counting under perturbations, and (4) generalization on pick-and-place of the lego block with real-world SO101. +

+ + +## Evaluate the finetuned model and run it in real-time + +Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./getting_started_real_world_robot#record-a-dataset). +Once you are logged in, you can run inference in your setup by doing: + +```bash +python -m lerobot.record \ + --robot.type=so101_follower \ + --robot.port=/dev/ttyACM0 \ # <- Use your port + --robot.id=my_blue_follower_arm \ # <- Use your robot id + --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras + --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording + --dataset.repo_id=${HF_USER}/eval_DATASET_NAME_test \ # <- This will be the dataset name on HF Hub + --dataset.episode_time_s=50 \ + --dataset.num_episodes=10 \ + --policy.path=HF_USER/FINETUNE_MODEL_NAME # <- Use your fine-tuned model +``` + +Depending on your evaluation setup, you can configure the duration and the number of episodes to record for your evaluation suite. diff --git a/docs/source/so100.mdx b/docs/source/so100.mdx new file mode 100644 index 0000000000000000000000000000000000000000..65849e950a66bb20cc7ab22f99de25ef3c473947 --- /dev/null +++ b/docs/source/so100.mdx @@ -0,0 +1 @@ +../../lerobot/common/robots/so100_follower/so100.mdx \ No newline at end of file diff --git a/docs/source/so101.mdx b/docs/source/so101.mdx new file mode 100644 index 0000000000000000000000000000000000000000..dc4720c285ef33c893268e7bd4aed06136a67acf --- /dev/null +++ b/docs/source/so101.mdx @@ -0,0 +1 @@ +../../lerobot/common/robots/so101_follower/so101.mdx \ No newline at end of file diff --git a/examples/1_load_lerobot_dataset.py b/examples/1_load_lerobot_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0325be040147c0bb4c995b26a07617d471b0011b --- /dev/null +++ b/examples/1_load_lerobot_dataset.py @@ -0,0 +1,148 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script demonstrates the use of `LeRobotDataset` class for handling and processing robotic datasets from Hugging Face. +It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch. + +Features included in this script: +- Viewing a dataset's metadata and exploring its properties. +- Loading an existing dataset from the hub or a subset of it. +- Accessing frames by episode number. +- Using advanced dataset features like timestamp-based frame selection. +- Demonstrating compatibility with PyTorch DataLoader for batch processing. + +The script ends with examples of how to batch process data using PyTorch's DataLoader. +""" + +from pprint import pprint + +import torch +from huggingface_hub import HfApi + +import lerobot +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata + +# We ported a number of existing datasets ourselves, use this to see the list: +print("List of available datasets:") +pprint(lerobot.available_datasets) + +# You can also browse through the datasets created/ported by the community on the hub using the hub api: +hub_api = HfApi() +repo_ids = [info.id for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])] +pprint(repo_ids) + +# Or simply explore them in your web browser directly at: +# https://huggingface.co/datasets?other=LeRobot + +# Let's take this one for this example +repo_id = "lerobot/aloha_mobile_cabinet" +# We can have a look and fetch its metadata to know more about it: +ds_meta = LeRobotDatasetMetadata(repo_id) + +# By instantiating just this class, you can quickly access useful information about the content and the +# structure of the dataset without downloading the actual data yet (only metadata files — which are +# lightweight). +print(f"Total number of episodes: {ds_meta.total_episodes}") +print(f"Average number of frames per episode: {ds_meta.total_frames / ds_meta.total_episodes:.3f}") +print(f"Frames per second used during data collection: {ds_meta.fps}") +print(f"Robot type: {ds_meta.robot_type}") +print(f"keys to access images from cameras: {ds_meta.camera_keys=}\n") + +print("Tasks:") +print(ds_meta.tasks) +print("Features:") +pprint(ds_meta.features) + +# You can also get a short summary by simply printing the object: +print(ds_meta) + +# You can then load the actual dataset from the hub. +# Either load any subset of episodes: +dataset = LeRobotDataset(repo_id, episodes=[0, 10, 11, 23]) + +# And see how many frames you have: +print(f"Selected episodes: {dataset.episodes}") +print(f"Number of episodes selected: {dataset.num_episodes}") +print(f"Number of frames selected: {dataset.num_frames}") + +# Or simply load the entire dataset: +dataset = LeRobotDataset(repo_id) +print(f"Number of episodes selected: {dataset.num_episodes}") +print(f"Number of frames selected: {dataset.num_frames}") + +# The previous metadata class is contained in the 'meta' attribute of the dataset: +print(dataset.meta) + +# LeRobotDataset actually wraps an underlying Hugging Face dataset +# (see https://huggingface.co/docs/datasets for more information). +print(dataset.hf_dataset) + +# LeRobot datasets also subclasses PyTorch datasets so you can do everything you know and love from working +# with the latter, like iterating through the dataset. +# The __getitem__ iterates over the frames of the dataset. Since our datasets are also structured by +# episodes, you can access the frame indices of any episode using the episode_data_index. Here, we access +# frame indices associated to the first episode: +episode_index = 0 +from_idx = dataset.episode_data_index["from"][episode_index].item() +to_idx = dataset.episode_data_index["to"][episode_index].item() + +# Then we grab all the image frames from the first camera: +camera_key = dataset.meta.camera_keys[0] +frames = [dataset[idx][camera_key] for idx in range(from_idx, to_idx)] + +# The objects returned by the dataset are all torch.Tensors +print(type(frames[0])) +print(frames[0].shape) + +# Since we're using pytorch, the shape is in pytorch, channel-first convention (c, h, w). +# We can compare this shape with the information available for that feature +pprint(dataset.features[camera_key]) +# In particular: +print(dataset.features[camera_key]["shape"]) +# The shape is in (h, w, c) which is a more universal format. + +# For many machine learning applications we need to load the history of past observations or trajectories of +# future actions. Our datasets can load previous and future frames for each key/modality, using timestamps +# differences with the current loaded frame. For instance: +delta_timestamps = { + # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame + camera_key: [-1, -0.5, -0.20, 0], + # loads 6 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame + "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, 0], + # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future + "action": [t / dataset.fps for t in range(64)], +} +# Note that in any case, these delta_timestamps values need to be multiples of (1/fps) so that added to any +# timestamp, you still get a valid timestamp. + +dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps) +print(f"\n{dataset[0][camera_key].shape=}") # (4, c, h, w) +print(f"{dataset[0]['observation.state'].shape=}") # (6, c) +print(f"{dataset[0]['action'].shape=}\n") # (64, c) + +# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers because they are just +# PyTorch datasets. +dataloader = torch.utils.data.DataLoader( + dataset, + num_workers=0, + batch_size=32, + shuffle=True, +) + +for batch in dataloader: + print(f"{batch[camera_key].shape=}") # (32, 4, c, h, w) + print(f"{batch['observation.state'].shape=}") # (32, 6, c) + print(f"{batch['action'].shape=}") # (32, 64, c) + break diff --git a/examples/2_evaluate_pretrained_policy.py b/examples/2_evaluate_pretrained_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..bf0f4666df7b22e6d2e64f87a46dff6e84bf05ea --- /dev/null +++ b/examples/2_evaluate_pretrained_policy.py @@ -0,0 +1,139 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local +training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first. + +It requires the installation of the 'gym_pusht' simulation environment. Install it by running: +```bash +pip install -e ".[pusht]" +``` +""" + +from pathlib import Path + +import gym_pusht # noqa: F401 +import gymnasium as gym +import imageio +import numpy +import torch + +from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy + +# Create a directory to store the video of the evaluation +output_directory = Path("outputs/eval/example_pusht_diffusion") +output_directory.mkdir(parents=True, exist_ok=True) + +# Select your device +device = "cuda" + +# Provide the [hugging face repo id](https://huggingface.co/lerobot/diffusion_pusht): +pretrained_policy_path = "lerobot/diffusion_pusht" +# OR a path to a local outputs/train folder. +# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion") + +policy = DiffusionPolicy.from_pretrained(pretrained_policy_path) + +# Initialize evaluation environment to render two observation types: +# an image of the scene and state/position of the agent. The environment +# also automatically stops running after 300 interactions/steps. +env = gym.make( + "gym_pusht/PushT-v0", + obs_type="pixels_agent_pos", + max_episode_steps=300, +) + +# We can verify that the shapes of the features expected by the policy match the ones from the observations +# produced by the environment +print(policy.config.input_features) +print(env.observation_space) + +# Similarly, we can check that the actions produced by the policy will match the actions expected by the +# environment +print(policy.config.output_features) +print(env.action_space) + +# Reset the policy and environments to prepare for rollout +policy.reset() +numpy_observation, info = env.reset(seed=42) + +# Prepare to collect every rewards and all the frames of the episode, +# from initial state to final state. +rewards = [] +frames = [] + +# Render frame of the initial state +frames.append(env.render()) + +step = 0 +done = False +while not done: + # Prepare observation for the policy running in Pytorch + state = torch.from_numpy(numpy_observation["agent_pos"]) + image = torch.from_numpy(numpy_observation["pixels"]) + + # Convert to float32 with image from channel first in [0,255] + # to channel last in [0,1] + state = state.to(torch.float32) + image = image.to(torch.float32) / 255 + image = image.permute(2, 0, 1) + + # Send data tensors from CPU to GPU + state = state.to(device, non_blocking=True) + image = image.to(device, non_blocking=True) + + # Add extra (empty) batch dimension, required to forward the policy + state = state.unsqueeze(0) + image = image.unsqueeze(0) + + # Create the policy input dictionary + observation = { + "observation.state": state, + "observation.image": image, + } + + # Predict the next action with respect to the current observation + with torch.inference_mode(): + action = policy.select_action(observation) + + # Prepare the action for the environment + numpy_action = action.squeeze(0).to("cpu").numpy() + + # Step through the environment and receive a new observation + numpy_observation, reward, terminated, truncated, info = env.step(numpy_action) + print(f"{step=} {reward=} {terminated=}") + + # Keep track of all the rewards and frames + rewards.append(reward) + frames.append(env.render()) + + # The rollout is considered done when the success state is reached (i.e. terminated is True), + # or the maximum number of iterations is reached (i.e. truncated is True) + done = terminated | truncated | done + step += 1 + +if terminated: + print("Success!") +else: + print("Failure!") + +# Get the speed of environment (i.e. its number of frames per second). +fps = env.metadata["render_fps"] + +# Encode all frames into a mp4 video. +video_path = output_directory / "rollout.mp4" +imageio.mimsave(str(video_path), numpy.stack(frames), fps=fps) + +print(f"Video of the evaluation is available in '{video_path}'.") diff --git a/examples/3_train_policy.py b/examples/3_train_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..cb8c633a5a9a7313fbfb5c4afc43f10cbc515eb0 --- /dev/null +++ b/examples/3_train_policy.py @@ -0,0 +1,120 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This script demonstrates how to train Diffusion Policy on the PushT environment. + +Once you have trained a model with this script, you can try to evaluate it on +examples/2_evaluate_pretrained_policy.py +""" + +from pathlib import Path + +import torch + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata +from lerobot.common.datasets.utils import dataset_to_policy_features +from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig +from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy +from lerobot.configs.types import FeatureType + + +def main(): + # Create a directory to store the training checkpoint. + output_directory = Path("outputs/train/example_pusht_diffusion") + output_directory.mkdir(parents=True, exist_ok=True) + + # # Select your device + device = torch.device("cuda") + + # Number of offline training steps (we'll only do offline training for this example.) + # Adjust as you prefer. 5000 steps are needed to get something worth evaluating. + training_steps = 5000 + log_freq = 1 + + # When starting from scratch (i.e. not from a pretrained policy), we need to specify 2 things before + # creating the policy: + # - input/output shapes: to properly size the policy + # - dataset stats: for normalization and denormalization of input/outputs + dataset_metadata = LeRobotDatasetMetadata("lerobot/pusht") + features = dataset_to_policy_features(dataset_metadata.features) + output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION} + input_features = {key: ft for key, ft in features.items() if key not in output_features} + + # Policies are initialized with a configuration class, in this case `DiffusionConfig`. For this example, + # we'll just use the defaults and so no arguments other than input/output features need to be passed. + cfg = DiffusionConfig(input_features=input_features, output_features=output_features) + + # We can now instantiate our policy with this config and the dataset stats. + policy = DiffusionPolicy(cfg, dataset_stats=dataset_metadata.stats) + policy.train() + policy.to(device) + + # Another policy-dataset interaction is with the delta_timestamps. Each policy expects a given number frames + # which can differ for inputs, outputs and rewards (if there are some). + delta_timestamps = { + "observation.image": [i / dataset_metadata.fps for i in cfg.observation_delta_indices], + "observation.state": [i / dataset_metadata.fps for i in cfg.observation_delta_indices], + "action": [i / dataset_metadata.fps for i in cfg.action_delta_indices], + } + + # In this case with the standard configuration for Diffusion Policy, it is equivalent to this: + delta_timestamps = { + # Load the previous image and state at -0.1 seconds before current frame, + # then load current image and state corresponding to 0.0 second. + "observation.image": [-0.1, 0.0], + "observation.state": [-0.1, 0.0], + # Load the previous action (-0.1), the next action to be executed (0.0), + # and 14 future actions with a 0.1 seconds spacing. All these actions will be + # used to supervise the policy. + "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4], + } + + # We can then instantiate the dataset with these delta_timestamps configuration. + dataset = LeRobotDataset("lerobot/pusht", delta_timestamps=delta_timestamps) + + # Then we create our optimizer and dataloader for offline training. + optimizer = torch.optim.Adam(policy.parameters(), lr=1e-4) + dataloader = torch.utils.data.DataLoader( + dataset, + num_workers=4, + batch_size=64, + shuffle=True, + pin_memory=device.type != "cpu", + drop_last=True, + ) + + # Run training loop. + step = 0 + done = False + while not done: + for batch in dataloader: + batch = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} + loss, _ = policy.forward(batch) + loss.backward() + optimizer.step() + optimizer.zero_grad() + + if step % log_freq == 0: + print(f"step: {step} loss: {loss.item():.3f}") + step += 1 + if step >= training_steps: + done = True + break + + # Save a policy checkpoint. + policy.save_pretrained(output_directory) + + +if __name__ == "__main__": + main() diff --git a/examples/4_train_policy_with_script.md b/examples/4_train_policy_with_script.md new file mode 100644 index 0000000000000000000000000000000000000000..8b90418809caf35314819a6c6c723c5fc8581bbf --- /dev/null +++ b/examples/4_train_policy_with_script.md @@ -0,0 +1,274 @@ +This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run. +> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu. + + +## The training script + +LeRobot offers a training script at [`lerobot/scripts/train.py`](../lerobot/scripts/train.py). At a high level it does the following: + +- Initialize/load a configuration for the following steps using. +- Instantiates a dataset. +- (Optional) Instantiates a simulation environment corresponding to that dataset. +- Instantiates a policy. +- Runs a standard training loop with forward pass, backward pass, optimization step, and occasional logging, evaluation (of the policy on the environment), and checkpointing. + +## Overview of the configuration system + +In the training script, the main function `train` expects a `TrainPipelineConfig` object: +```python +# train.py +@parser.wrap() +def train(cfg: TrainPipelineConfig): +``` + +You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option) + +When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.) + +Let's have a look at a simplified example. Amongst other attributes, the training config has the following attributes: +```python +@dataclass +class TrainPipelineConfig: + dataset: DatasetConfig + env: envs.EnvConfig | None = None + policy: PreTrainedConfig | None = None +``` +in which `DatasetConfig` for example is defined as such: +```python +@dataclass +class DatasetConfig: + repo_id: str + episodes: list[int] | None = None + video_backend: str = "pyav" +``` + +This creates a hierarchical relationship where, for example assuming we have a `cfg` instance of `TrainPipelineConfig`, we can access the `repo_id` value with `cfg.dataset.repo_id`. +From the command line, we can specify this value by using a very similar syntax `--dataset.repo_id=repo/id`. + +By default, every field takes its default value specified in the dataclass. If a field doesn't have a default value, it needs to be specified either from the command line or from a config file – which path is also given in the command line (more in this below). In the example above, the `dataset` field doesn't have a default value which means it must be specified. + + +## Specifying values from the CLI + +Let's say that we want to train [Diffusion Policy](../lerobot/common/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this: +```bash +python lerobot/scripts/train.py \ + --dataset.repo_id=lerobot/pusht \ + --policy.type=diffusion \ + --env.type=pusht +``` + +Let's break this down: +- To specify the dataset, we just need to specify its `repo_id` on the hub which is the only required argument in the `DatasetConfig`. The rest of the fields have default values and in this case we are fine with those so we can just add the option `--dataset.repo_id=lerobot/pusht`. +- To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/common/policies](../lerobot/common/policies) +- Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/common/envs/configs.py`](../lerobot/common/envs/configs.py) + +Let's see another example. Let's say you've been training [ACT](../lerobot/common/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with: +```bash +python lerobot/scripts/train.py \ + --policy.type=act \ + --dataset.repo_id=lerobot/aloha_sim_insertion_human \ + --env.type=aloha \ + --output_dir=outputs/train/act_aloha_insertion +``` +> Notice we added `--output_dir` to explicitly tell where to write outputs from this run (checkpoints, training state, configs etc.). This is not mandatory and if you don't specify it, a default directory will be created from the current date and time, env.type and policy.type. This will typically look like `outputs/train/2025-01-24/16-10-05_aloha_act`. + +We now want to train a different policy for aloha on another task. We'll change the dataset and use [lerobot/aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) instead. Of course, we also need to change the task of the environment as well to match this other task. +Looking at the [`AlohaEnv`](../lerobot/common/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using: +```bash +python lerobot/scripts/train.py \ + --policy.type=act \ + --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \ + --env.type=aloha \ + --env.task=AlohaTransferCube-v0 \ + --output_dir=outputs/train/act_aloha_transfer +``` + +## Loading from a config file + +Now, let's assume that we want to reproduce the run just above. That run has produced a `train_config.json` file in its checkpoints, which serializes the `TrainPipelineConfig` instance it used: +```json +{ + "dataset": { + "repo_id": "lerobot/aloha_sim_transfer_cube_human", + "episodes": null, + ... + }, + "env": { + "type": "aloha", + "task": "AlohaTransferCube-v0", + "fps": 50, + ... + }, + "policy": { + "type": "act", + "n_obs_steps": 1, + ... + }, + ... +} +``` + +We can then simply load the config values from this file using: +```bash +python lerobot/scripts/train.py \ + --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \ + --output_dir=outputs/train/act_aloha_transfer_2 +``` +`--config_path` is also a special argument which allows to initialize the config from a local config file. It can point to a directory that contains `train_config.json` or to the config file itself directly. + +Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.: +```bash +python lerobot/scripts/train.py \ + --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \ + --output_dir=outputs/train/act_aloha_transfer_2 + --policy.n_action_steps=80 +``` +> Note: While `--output_dir` is not required in general, in this case we need to specify it since it will otherwise take the value from the `train_config.json` (which is `outputs/train/act_aloha_transfer`). In order to prevent accidental deletion of previous run checkpoints, we raise an error if you're trying to write in an existing directory. This is not the case when resuming a run, which is what you'll learn next. + +`--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running: +```bash +python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht +``` +will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht) + + +## Resume training + +Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to do that here. + +Let's reuse the command from the previous run and add a few more options: +```bash +python lerobot/scripts/train.py \ + --policy.type=act \ + --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \ + --env.type=aloha \ + --env.task=AlohaTransferCube-v0 \ + --log_freq=25 \ + --save_freq=100 \ + --output_dir=outputs/train/run_resumption +``` + +Here we've taken care to set up the log frequency and checkpointing frequency to low numbers so we can showcase resumption. You should be able to see some logging and have a first checkpoint within 1 minute (depending on hardware). Wait for the first checkpoint to happen, you should see a line that looks like this in your terminal: +``` +INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100 +``` +Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with: +```bash +python lerobot/scripts/train.py \ + --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \ + --resume=true +``` +You should see from the logging that your training picks up from where it left off. + +Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default. +You could double the number of steps of the previous run with: +```bash +python lerobot/scripts/train.py \ + --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \ + --resume=true \ + --steps=200000 +``` + +## Outputs of a run +In the output directory, there will be a folder called `checkpoints` with the following structure: +```bash +outputs/train/run_resumption/checkpoints +├── 000100 # checkpoint_dir for training step 100 +│ ├── pretrained_model/ +│ │ ├── config.json # policy config +│ │ ├── model.safetensors # policy weights +│ │ └── train_config.json # train config +│ └── training_state/ +│ ├── optimizer_param_groups.json # optimizer param groups +│ ├── optimizer_state.safetensors # optimizer state +│ ├── rng_state.safetensors # rng states +│ ├── scheduler_state.json # scheduler state +│ └── training_step.json # training step +├── 000200 +└── last -> 000200 # symlink to the last available checkpoint +``` + +## Fine-tuning a pre-trained policy + +In addition to the features currently in Draccus, we've added a special `.path` argument for the policy, which allows to load a policy as you would with `PreTrainedPolicy.from_pretrained()`. In that case, `path` can be a local directory that contains a checkpoint or a repo_id pointing to a pretrained policy on the hub. + +For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with: +```bash +python lerobot/scripts/train.py \ + --policy.path=lerobot/act_aloha_sim_transfer_cube_human \ + --dataset.repo_id=lerobot/aloha_sim_insertion_human \ + --env.type=aloha \ + --env.task=AlohaInsertion-v0 +``` + +When doing so, keep in mind that the features of the fine-tuning dataset would have to match the input/output features of the pretrained policy. + +## Typical logs and metrics + +When you start the training process, you will first see your full configuration being printed in the terminal. You can check it to make sure that you configured your run correctly. The final configuration will also be saved with the checkpoint. + +After that, you will see training log like this one: +``` +INFO 2024-08-14 13:35:12 ts/train.py:192 step:0 smpl:64 ep:1 epch:0.00 loss:1.112 grdn:15.387 lr:2.0e-07 updt_s:1.738 data_s:4.774 +``` +or evaluation log: +``` +INFO 2024-08-14 13:38:45 ts/train.py:226 step:100 smpl:6K ep:52 epch:0.25 ∑rwrd:20.693 success:0.0% eval_s:120.266 +``` + +These logs will also be saved in wandb if `wandb.enable` is set to `true`. Here are the meaning of some abbreviations: +- `smpl`: number of samples seen during training. +- `ep`: number of episodes seen during training. An episode contains multiple samples in a complete manipulation task. +- `epch`: number of time all unique samples are seen (epoch). +- `grdn`: gradient norm. +- `∑rwrd`: compute the sum of rewards in every evaluation episode and then take an average of them. +- `success`: average success rate of eval episodes. Reward and success are usually different except for the sparsing reward setting, where reward=1 only when the task is completed successfully. +- `eval_s`: time to evaluate the policy in the environment, in second. +- `updt_s`: time to update the network parameters, in second. +- `data_s`: time to load a batch of data, in second. + +Some metrics are useful for initial performance profiling. For example, if you find the current GPU utilization is low via the `nvidia-smi` command and `data_s` sometimes is too high, you may need to modify batch size or number of dataloading workers to accelerate dataloading. We also recommend [pytorch profiler](https://github.com/huggingface/lerobot?tab=readme-ov-file#improve-your-code-with-profiling) for detailed performance probing. + +## In short + +We'll summarize here the main use cases to remember from this tutorial. + +#### Train a policy from scratch – CLI +```bash +python lerobot/scripts/train.py \ + --policy.type=act \ # <- select 'act' policy + --env.type=pusht \ # <- select 'pusht' environment + --dataset.repo_id=lerobot/pusht # <- train on this dataset +``` + +#### Train a policy from scratch - config file + CLI +```bash +python lerobot/scripts/train.py \ + --config_path=path/to/pretrained_model \ # <- can also be a repo_id + --policy.n_action_steps=80 # <- you may still override values +``` + +#### Resume/continue a training run +```bash +python lerobot/scripts/train.py \ + --config_path=checkpoint/pretrained_model/ \ + --resume=true \ + --steps=200000 # <- you can change some training parameters +``` + +#### Fine-tuning +```bash +python lerobot/scripts/train.py \ + --policy.path=lerobot/act_aloha_sim_transfer_cube_human \ # <- can also be a local path to a checkpoint + --dataset.repo_id=lerobot/aloha_sim_insertion_human \ + --env.type=aloha \ + --env.task=AlohaInsertion-v0 +``` + +--- + +Now that you know the basics of how to train a policy, you might want to know how to apply this knowledge to actual robots, or how to record your own datasets and train policies on your specific task? +If that's the case, head over to the next tutorial [`7_get_started_with_real_robot.md`](./7_get_started_with_real_robot.md). + +Or in the meantime, happy training! 🤗 diff --git a/examples/advanced/1_add_image_transforms.py b/examples/advanced/1_add_image_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..1a0aeb220e7da0d48a07ad109e3e18d0284272ad --- /dev/null +++ b/examples/advanced/1_add_image_transforms.py @@ -0,0 +1,67 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script demonstrates how to use torchvision's image transformation with LeRobotDataset for data +augmentation purposes. The transformations are passed to the dataset as an argument upon creation, and +transforms are applied to the observation images before they are returned in the dataset's __getitem__. +""" + +from pathlib import Path + +from torchvision.transforms import ToPILImage, v2 + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + +dataset_repo_id = "lerobot/aloha_static_screw_driver" + +# Create a LeRobotDataset with no transformations +dataset = LeRobotDataset(dataset_repo_id, episodes=[0]) +# This is equivalent to `dataset = LeRobotDataset(dataset_repo_id, image_transforms=None)` + +# Get the index of the first observation in the first episode +first_idx = dataset.episode_data_index["from"][0].item() + +# Get the frame corresponding to the first camera +frame = dataset[first_idx][dataset.meta.camera_keys[0]] + + +# Define the transformations +transforms = v2.Compose( + [ + v2.ColorJitter(brightness=(0.5, 1.5)), + v2.ColorJitter(contrast=(0.5, 1.5)), + v2.ColorJitter(hue=(-0.1, 0.1)), + v2.RandomAdjustSharpness(sharpness_factor=2, p=1), + ] +) + +# Create another LeRobotDataset with the defined transformations +transformed_dataset = LeRobotDataset(dataset_repo_id, episodes=[0], image_transforms=transforms) + +# Get a frame from the transformed dataset +transformed_frame = transformed_dataset[first_idx][transformed_dataset.meta.camera_keys[0]] + +# Create a directory to store output images +output_dir = Path("outputs/image_transforms") +output_dir.mkdir(parents=True, exist_ok=True) + +# Save the original frame +to_pil = ToPILImage() +to_pil(frame).save(output_dir / "original_frame.png", quality=100) +print(f"Original frame saved to {output_dir / 'original_frame.png'}.") + +# Save the transformed frame +to_pil(transformed_frame).save(output_dir / "transformed_frame.png", quality=100) +print(f"Transformed frame saved to {output_dir / 'transformed_frame.png'}.") diff --git a/examples/advanced/2_calculate_validation_loss.py b/examples/advanced/2_calculate_validation_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..fc8743a9f79046e905c18ac1866101e8a35f9457 --- /dev/null +++ b/examples/advanced/2_calculate_validation_loss.py @@ -0,0 +1,104 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This script demonstrates how to slice a dataset and calculate the loss on a subset of the data. + +This technique can be useful for debugging and testing purposes, as well as identifying whether a policy +is learning effectively. + +Furthermore, relying on validation loss to evaluate performance is generally not considered a good practice, +especially in the context of imitation learning. The most reliable approach is to evaluate the policy directly +on the target environment, whether that be in simulation or the real world. +""" + +import math + +import torch + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata +from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy + + +def main(): + device = torch.device("cuda") + + # Download the diffusion policy for pusht environment + pretrained_policy_path = "lerobot/diffusion_pusht" + # OR uncomment the following to evaluate a policy from the local outputs/train folder. + # pretrained_policy_path = Path("outputs/train/example_pusht_diffusion") + + policy = DiffusionPolicy.from_pretrained(pretrained_policy_path) + policy.eval() + policy.to(device) + + # Set up the dataset. + delta_timestamps = { + # Load the previous image and state at -0.1 seconds before current frame, + # then load current image and state corresponding to 0.0 second. + "observation.image": [-0.1, 0.0], + "observation.state": [-0.1, 0.0], + # Load the previous action (-0.1), the next action to be executed (0.0), + # and 14 future actions with a 0.1 seconds spacing. All these actions will be + # used to calculate the loss. + "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4], + } + + # Load the last 10% of episodes of the dataset as a validation set. + # - Load dataset metadata + dataset_metadata = LeRobotDatasetMetadata("lerobot/pusht") + # - Calculate train and val episodes + total_episodes = dataset_metadata.total_episodes + episodes = list(range(dataset_metadata.total_episodes)) + num_train_episodes = math.floor(total_episodes * 90 / 100) + train_episodes = episodes[:num_train_episodes] + val_episodes = episodes[num_train_episodes:] + print(f"Number of episodes in full dataset: {total_episodes}") + print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}") + print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}") + # - Load train and val datasets + train_dataset = LeRobotDataset( + "lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps + ) + val_dataset = LeRobotDataset("lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps) + print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}") + print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}") + + # Create dataloader for evaluation. + val_dataloader = torch.utils.data.DataLoader( + val_dataset, + num_workers=4, + batch_size=64, + shuffle=False, + pin_memory=device != torch.device("cpu"), + drop_last=False, + ) + + # Run validation loop. + loss_cumsum = 0 + n_examples_evaluated = 0 + for batch in val_dataloader: + batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()} + loss, _ = policy.forward(batch) + + loss_cumsum += loss.item() + n_examples_evaluated += batch["index"].shape[0] + + # Calculate the average loss over the validation set. + average_loss = loss_cumsum / n_examples_evaluated + + print(f"Average loss on validation set: {average_loss:.4f}") + + +if __name__ == "__main__": + main() diff --git a/examples/backward_compatibility/replay.py b/examples/backward_compatibility/replay.py new file mode 100644 index 0000000000000000000000000000000000000000..71af1feef7982b3005136b91d9850bdef8622637 --- /dev/null +++ b/examples/backward_compatibility/replay.py @@ -0,0 +1,105 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Replays the actions of an episode from a dataset on a robot. + +Example: + +```shell +python -m lerobot.replay \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=black \ + --dataset.repo_id=aliberts/record-test \ + --dataset.episode=2 +``` +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pathlib import Path +from pprint import pformat + +import draccus + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.robots import ( # noqa: F401 + Robot, + RobotConfig, + koch_follower, + make_robot_from_config, + so100_follower, + so101_follower, +) +from lerobot.common.utils.robot_utils import busy_wait +from lerobot.common.utils.utils import ( + init_logging, + log_say, +) + + +@dataclass +class DatasetReplayConfig: + # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`). + repo_id: str + # Episode to replay. + episode: int + # Root directory where the dataset will be stored (e.g. 'dataset/path'). + root: str | Path | None = None + # Limit the frames per second. By default, uses the policy fps. + fps: int = 30 + + +@dataclass +class ReplayConfig: + robot: RobotConfig + dataset: DatasetReplayConfig + # Use vocal synthesis to read events. + play_sounds: bool = True + + +@draccus.wrap() +def replay(cfg: ReplayConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + + robot = make_robot_from_config(cfg.robot) + dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode]) + actions = dataset.hf_dataset.select_columns("action") + robot.connect() + + log_say("Replaying episode", cfg.play_sounds, blocking=True) + for idx in range(dataset.num_frames): + start_episode_t = time.perf_counter() + + action_array = actions[idx]["action"] + action = {} + for i, name in enumerate(dataset.features["action"]["names"]): + key = f"{name.removeprefix('main_')}.pos" + action[key] = action_array[i].item() + + action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90) + action["elbow_flex.pos"] -= 90 + robot.send_action(action) + + dt_s = time.perf_counter() - start_episode_t + busy_wait(1 / dataset.fps - dt_s) + + robot.disconnect() + + +if __name__ == "__main__": + replay() diff --git a/examples/lekiwi/evaluate.py b/examples/lekiwi/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..c9102bd1e6aec36b24bbe4215c8081eb1d86a7b3 --- /dev/null +++ b/examples/lekiwi/evaluate.py @@ -0,0 +1,32 @@ +from lerobot.common.datasets.utils import build_dataset_frame, hw_to_dataset_features +from lerobot.common.policies.act.modeling_act import ACTPolicy +from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig +from lerobot.common.utils.control_utils import predict_action +from lerobot.common.utils.utils import get_safe_torch_device + +NB_CYCLES_CLIENT_CONNECTION = 1000 + +robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") +robot = LeKiwiClient(robot_config) + +robot.connect() + +policy = ACTPolicy.from_pretrained("pepijn223/act_lekiwi_circle") +policy.reset() + +obs_features = hw_to_dataset_features(robot.observation_features, "observation") + +print("Running inference") +i = 0 +while i < NB_CYCLES_CLIENT_CONNECTION: + obs = robot.get_observation() + + observation_frame = build_dataset_frame(obs_features, obs, prefix="observation") + action_values = predict_action( + observation_frame, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp + ) + action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)} + robot.send_action(action) + i += 1 + +robot.disconnect() diff --git a/examples/lekiwi/record.py b/examples/lekiwi/record.py new file mode 100644 index 0000000000000000000000000000000000000000..5e9c8d444ded65f98e0ba5610b72250636672a30 --- /dev/null +++ b/examples/lekiwi/record.py @@ -0,0 +1,67 @@ +import time + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.utils import hw_to_dataset_features +from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig +from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient +from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig +from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig + +NB_CYCLES_CLIENT_CONNECTION = 250 + +leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760431551") +leader_arm = SO100Leader(leader_arm_config) + +keyboard_config = KeyboardTeleopConfig() +keyboard = KeyboardTeleop(keyboard_config) + +robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") +robot = LeKiwiClient(robot_config) + +action_features = hw_to_dataset_features(robot.action_features, "action") +obs_features = hw_to_dataset_features(robot.observation_features, "observation") +dataset_features = {**action_features, **obs_features} + +dataset = LeRobotDataset.create( + repo_id="pepijn223/lekiwi" + str(int(time.time())), + fps=10, + features=dataset_features, + robot_type=robot.name, +) + +leader_arm.connect() +keyboard.connect() +robot.connect() + +if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected: + exit() + +print("Starting LeKiwi recording") +i = 0 +while i < NB_CYCLES_CLIENT_CONNECTION: + arm_action = leader_arm.get_action() + arm_action = {f"arm_{k}": v for k, v in arm_action.items()} + + keyboard_keys = keyboard.get_action() + + base_action = robot._from_keyboard_to_base_action(keyboard_keys) + + action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action + + action_sent = robot.send_action(action) + observation = robot.get_observation() + + frame = {**action_sent, **observation} + task = "Dummy Example Task Dataset" + + dataset.add_frame(frame, task) + i += 1 + +print("Disconnecting Teleop Devices and LeKiwi Client") +robot.disconnect() +leader_arm.disconnect() +keyboard.disconnect() + +print("Uploading dataset to the hub") +dataset.save_episode() +dataset.push_to_hub() diff --git a/examples/lekiwi/replay.py b/examples/lekiwi/replay.py new file mode 100644 index 0000000000000000000000000000000000000000..6456d65f99d9d6b179e062525676da75e92ecc5d --- /dev/null +++ b/examples/lekiwi/replay.py @@ -0,0 +1,25 @@ +import time + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig +from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient +from lerobot.common.utils.robot_utils import busy_wait + +robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") +robot = LeKiwiClient(robot_config) + +dataset = LeRobotDataset("pepijn223/lekiwi1749025613", episodes=[0]) + +robot.connect() + +print("Replaying episode…") +for _, action_array in enumerate(dataset.hf_dataset["action"]): + t0 = time.perf_counter() + + action = {name: float(action_array[i]) for i, name in enumerate(dataset.features["action"]["names"])} + robot.send_action(action) + + busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) + +print("Disconnecting LeKiwi Client") +robot.disconnect() diff --git a/examples/lekiwi/teleoperate.py b/examples/lekiwi/teleoperate.py new file mode 100644 index 0000000000000000000000000000000000000000..12a897018182b47b2eac5053c2072bbc33fbb7cc --- /dev/null +++ b/examples/lekiwi/teleoperate.py @@ -0,0 +1,32 @@ +from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig +from lerobot.common.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig +from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig + +robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi") + +teleop__arm_config = SO100LeaderConfig( + port="/dev/tty.usbmodem58760431551", + id="my_awesome_leader_arm", +) + +teleop_keyboard_config = KeyboardTeleopConfig( + id="my_laptop_keyboard", +) + +robot = LeKiwiClient(robot_config) +teleop_arm = SO100Leader(teleop__arm_config) +telep_keyboard = KeyboardTeleop(teleop_keyboard_config) +robot.connect() +teleop_arm.connect() +telep_keyboard.connect() + +while True: + observation = robot.get_observation() + + arm_action = teleop_arm.get_action() + arm_action = {f"arm_{k}": v for k, v in arm_action.items()} + + keyboard_keys = telep_keyboard.get_action() + base_action = robot._from_keyboard_to_base_action(keyboard_keys) + + robot.send_action(arm_action | base_action) diff --git a/lerobot/__init__.py b/lerobot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b7c3f7f58f2006986987300729f31f702aee1498 --- /dev/null +++ b/lerobot/__init__.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This file contains lists of available environments, dataset and policies to reflect the current state of LeRobot library. +We do not want to import all the dependencies, but instead we keep it lightweight to ensure fast access to these variables. + +Example: + ```python + import lerobot + print(lerobot.available_envs) + print(lerobot.available_tasks_per_env) + print(lerobot.available_datasets) + print(lerobot.available_datasets_per_env) + print(lerobot.available_real_world_datasets) + print(lerobot.available_policies) + print(lerobot.available_policies_per_env) + print(lerobot.available_robots) + print(lerobot.available_cameras) + print(lerobot.available_motors) + ``` + +When implementing a new dataset loadable with LeRobotDataset follow these steps: +- Update `available_datasets_per_env` in `lerobot/__init__.py` + +When implementing a new environment (e.g. `gym_aloha`), follow these steps: +- Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py` + +When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps: +- Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py` +- Set the required `name` class attribute. +- Update variables in `tests/test_available.py` by importing your new Policy class +""" + +import itertools + +from lerobot.__version__ import __version__ # noqa: F401 + +# TODO(rcadene): Improve policies and envs. As of now, an item in `available_policies` +# refers to a yaml file AND a modeling name. Same for `available_envs` which refers to +# a yaml file AND a environment name. The difference should be more obvious. +available_tasks_per_env = { + "aloha": [ + "AlohaInsertion-v0", + "AlohaTransferCube-v0", + ], + "pusht": ["PushT-v0"], + "xarm": ["XarmLift-v0"], +} +available_envs = list(available_tasks_per_env.keys()) + +available_datasets_per_env = { + "aloha": [ + "lerobot/aloha_sim_insertion_human", + "lerobot/aloha_sim_insertion_scripted", + "lerobot/aloha_sim_transfer_cube_human", + "lerobot/aloha_sim_transfer_cube_scripted", + "lerobot/aloha_sim_insertion_human_image", + "lerobot/aloha_sim_insertion_scripted_image", + "lerobot/aloha_sim_transfer_cube_human_image", + "lerobot/aloha_sim_transfer_cube_scripted_image", + ], + # TODO(alexander-soare): Add "lerobot/pusht_keypoints". Right now we can't because this is too tightly + # coupled with tests. + "pusht": ["lerobot/pusht", "lerobot/pusht_image"], + "xarm": [ + "lerobot/xarm_lift_medium", + "lerobot/xarm_lift_medium_replay", + "lerobot/xarm_push_medium", + "lerobot/xarm_push_medium_replay", + "lerobot/xarm_lift_medium_image", + "lerobot/xarm_lift_medium_replay_image", + "lerobot/xarm_push_medium_image", + "lerobot/xarm_push_medium_replay_image", + ], +} + +available_real_world_datasets = [ + "lerobot/aloha_mobile_cabinet", + "lerobot/aloha_mobile_chair", + "lerobot/aloha_mobile_elevator", + "lerobot/aloha_mobile_shrimp", + "lerobot/aloha_mobile_wash_pan", + "lerobot/aloha_mobile_wipe_wine", + "lerobot/aloha_static_battery", + "lerobot/aloha_static_candy", + "lerobot/aloha_static_coffee", + "lerobot/aloha_static_coffee_new", + "lerobot/aloha_static_cups_open", + "lerobot/aloha_static_fork_pick_up", + "lerobot/aloha_static_pingpong_test", + "lerobot/aloha_static_pro_pencil", + "lerobot/aloha_static_screw_driver", + "lerobot/aloha_static_tape", + "lerobot/aloha_static_thread_velcro", + "lerobot/aloha_static_towel", + "lerobot/aloha_static_vinh_cup", + "lerobot/aloha_static_vinh_cup_left", + "lerobot/aloha_static_ziploc_slide", + "lerobot/umi_cup_in_the_wild", + "lerobot/unitreeh1_fold_clothes", + "lerobot/unitreeh1_rearrange_objects", + "lerobot/unitreeh1_two_robot_greeting", + "lerobot/unitreeh1_warehouse", + "lerobot/nyu_rot_dataset", + "lerobot/utokyo_saytap", + "lerobot/imperialcollege_sawyer_wrist_cam", + "lerobot/utokyo_xarm_bimanual", + "lerobot/tokyo_u_lsmo", + "lerobot/utokyo_pr2_opening_fridge", + "lerobot/cmu_franka_exploration_dataset", + "lerobot/cmu_stretch", + "lerobot/asu_table_top", + "lerobot/utokyo_pr2_tabletop_manipulation", + "lerobot/utokyo_xarm_pick_and_place", + "lerobot/ucsd_kitchen_dataset", + "lerobot/austin_buds_dataset", + "lerobot/dlr_sara_grid_clamp", + "lerobot/conq_hose_manipulation", + "lerobot/columbia_cairlab_pusht_real", + "lerobot/dlr_sara_pour", + "lerobot/dlr_edan_shared_control", + "lerobot/ucsd_pick_and_place_dataset", + "lerobot/berkeley_cable_routing", + "lerobot/nyu_franka_play_dataset", + "lerobot/austin_sirius_dataset", + "lerobot/cmu_play_fusion", + "lerobot/berkeley_gnm_sac_son", + "lerobot/nyu_door_opening_surprising_effectiveness", + "lerobot/berkeley_fanuc_manipulation", + "lerobot/jaco_play", + "lerobot/viola", + "lerobot/kaist_nonprehensile", + "lerobot/berkeley_mvp", + "lerobot/uiuc_d3field", + "lerobot/berkeley_gnm_recon", + "lerobot/austin_sailor_dataset", + "lerobot/utaustin_mutex", + "lerobot/roboturk", + "lerobot/stanford_hydra_dataset", + "lerobot/berkeley_autolab_ur5", + "lerobot/stanford_robocook", + "lerobot/toto", + "lerobot/fmb", + "lerobot/droid_100", + "lerobot/berkeley_rpt", + "lerobot/stanford_kuka_multimodal_dataset", + "lerobot/iamlab_cmu_pickup_insert", + "lerobot/taco_play", + "lerobot/berkeley_gnm_cory_hall", + "lerobot/usc_cloth_sim", +] + +available_datasets = sorted( + set(itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets)) +) + +# lists all available policies from `lerobot/common/policies` +available_policies = ["act", "diffusion", "tdmpc", "vqbet"] + +# lists all available robots from `lerobot/common/robot_devices/robots` +available_robots = [ + "koch", + "koch_bimanual", + "aloha", + "so100", + "so101", +] + +# lists all available cameras from `lerobot/common/robot_devices/cameras` +available_cameras = [ + "opencv", + "intelrealsense", +] + +# lists all available motors from `lerobot/common/robot_devices/motors` +available_motors = [ + "dynamixel", + "feetech", +] + +# keys and values refer to yaml files +available_policies_per_env = { + "aloha": ["act"], + "pusht": ["diffusion", "vqbet"], + "xarm": ["tdmpc"], + "koch_real": ["act_koch_real"], + "aloha_real": ["act_aloha_real"], +} + +env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks] +env_dataset_pairs = [ + (env, dataset) for env, datasets in available_datasets_per_env.items() for dataset in datasets +] +env_dataset_policy_triplets = [ + (env, dataset, policy) + for env, datasets in available_datasets_per_env.items() + for dataset in datasets + for policy in available_policies_per_env[env] +] diff --git a/lerobot/__version__.py b/lerobot/__version__.py new file mode 100644 index 0000000000000000000000000000000000000000..90ee361a0b410c532dcb6ce2b51cd507fc86c4a5 --- /dev/null +++ b/lerobot/__version__.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""To enable `lerobot.__version__`""" + +from importlib.metadata import PackageNotFoundError, version + +try: + __version__ = version("lerobot") +except PackageNotFoundError: + __version__ = "unknown" diff --git a/lerobot/calibrate.py b/lerobot/calibrate.py new file mode 100644 index 0000000000000000000000000000000000000000..358f8b8f9804d7900a492fb26ca0ca845484ed53 --- /dev/null +++ b/lerobot/calibrate.py @@ -0,0 +1,84 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Helper to recalibrate your device (robot or teleoperator). + +Example: + +```shell +python -m lerobot.calibrate \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue +``` +""" + +import logging +from dataclasses import asdict, dataclass +from pprint import pformat + +import draccus + +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 +from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 +from lerobot.common.robots import ( # noqa: F401 + Robot, + RobotConfig, + koch_follower, + lekiwi, + make_robot_from_config, + so100_follower, + so101_follower, +) +from lerobot.common.teleoperators import ( # noqa: F401 + Teleoperator, + TeleoperatorConfig, + koch_leader, + make_teleoperator_from_config, + so100_leader, + so101_leader, +) +from lerobot.common.utils.utils import init_logging + + +@dataclass +class CalibrateConfig: + teleop: TeleoperatorConfig | None = None + robot: RobotConfig | None = None + + def __post_init__(self): + if bool(self.teleop) == bool(self.robot): + raise ValueError("Choose either a teleop or a robot.") + + self.device = self.robot if self.robot else self.teleop + + +@draccus.wrap() +def calibrate(cfg: CalibrateConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + + if isinstance(cfg.device, RobotConfig): + device = make_robot_from_config(cfg.device) + elif isinstance(cfg.device, TeleoperatorConfig): + device = make_teleoperator_from_config(cfg.device) + + device.connect(calibrate=False) + device.calibrate() + device.disconnect() + + +if __name__ == "__main__": + calibrate() diff --git a/lerobot/common/cameras/__init__.py b/lerobot/common/cameras/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..44cfbb7aaea3603881724a4a541ff8c7c657b010 --- /dev/null +++ b/lerobot/common/cameras/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .camera import Camera +from .configs import CameraConfig, ColorMode, Cv2Rotation +from .utils import make_cameras_from_configs diff --git a/lerobot/common/cameras/camera.py b/lerobot/common/cameras/camera.py new file mode 100644 index 0000000000000000000000000000000000000000..5ee6dc7b4b9214b074841d4349810153b906c7e7 --- /dev/null +++ b/lerobot/common/cameras/camera.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from typing import Any, Dict, List + +import numpy as np + +from .configs import CameraConfig, ColorMode + + +class Camera(abc.ABC): + """Base class for camera implementations. + + Defines a standard interface for camera operations across different backends. + Subclasses must implement all abstract methods. + + Manages basic camera properties (FPS, resolution) and core operations: + - Connection/disconnection + - Frame capture (sync/async) + + Attributes: + fps (int | None): Configured frames per second + width (int | None): Frame width in pixels + height (int | None): Frame height in pixels + + Example: + class MyCamera(Camera): + def __init__(self, config): ... + @property + def is_connected(self) -> bool: ... + def connect(self, warmup=True): ... + # Plus other required methods + """ + + def __init__(self, config: CameraConfig): + """Initialize the camera with the given configuration. + + Args: + config: Camera configuration containing FPS and resolution. + """ + self.fps: int | None = config.fps + self.width: int | None = config.width + self.height: int | None = config.height + + @property + @abc.abstractmethod + def is_connected(self) -> bool: + """Check if the camera is currently connected. + + Returns: + bool: True if the camera is connected and ready to capture frames, + False otherwise. + """ + pass + + @staticmethod + @abc.abstractmethod + def find_cameras() -> List[Dict[str, Any]]: + """Detects available cameras connected to the system. + Returns: + List[Dict[str, Any]]: A list of dictionaries, + where each dictionary contains information about a detected camera. + """ + pass + + @abc.abstractmethod + def connect(self, warmup: bool = True) -> None: + """Establish connection to the camera. + + Args: + warmup: If True (default), captures a warmup frame before returning. Useful + for cameras that require time to adjust capture settings. + If False, skips the warmup frame. + """ + pass + + @abc.abstractmethod + def read(self, color_mode: ColorMode | None = None) -> np.ndarray: + """Capture and return a single frame from the camera. + + Args: + color_mode: Desired color mode for the output frame. If None, + uses the camera's default color mode. + + Returns: + np.ndarray: Captured frame as a numpy array. + """ + pass + + @abc.abstractmethod + def async_read(self, timeout_ms: float = ...) -> np.ndarray: + """Asynchronously capture and return a single frame from the camera. + + Args: + timeout_ms: Maximum time to wait for a frame in milliseconds. + Defaults to implementation-specific timeout. + + Returns: + np.ndarray: Captured frame as a numpy array. + """ + pass + + @abc.abstractmethod + def disconnect(self) -> None: + """Disconnect from the camera and release resources.""" + pass diff --git a/lerobot/common/cameras/configs.py b/lerobot/common/cameras/configs.py new file mode 100644 index 0000000000000000000000000000000000000000..b83cf2415330fab0d7426c898cb1b4ec3115c22a --- /dev/null +++ b/lerobot/common/cameras/configs.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from dataclasses import dataclass +from enum import Enum + +import draccus + + +class ColorMode(str, Enum): + RGB = "rgb" + BGR = "bgr" + + +class Cv2Rotation(int, Enum): + NO_ROTATION = 0 + ROTATE_90 = 90 + ROTATE_180 = 180 + ROTATE_270 = -90 + + +@dataclass(kw_only=True) +class CameraConfig(draccus.ChoiceRegistry, abc.ABC): + fps: int | None = None + width: int | None = None + height: int | None = None + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) diff --git a/lerobot/common/cameras/opencv/__init__.py b/lerobot/common/cameras/opencv/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0b980f2f4692ab870354d398bba513ad15f006f6 --- /dev/null +++ b/lerobot/common/cameras/opencv/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .camera_opencv import OpenCVCamera +from .configuration_opencv import OpenCVCameraConfig diff --git a/lerobot/common/cameras/opencv/camera_opencv.py b/lerobot/common/cameras/opencv/camera_opencv.py new file mode 100644 index 0000000000000000000000000000000000000000..2550a5abb2d38df97fc2267ce95b920e11f57fde --- /dev/null +++ b/lerobot/common/cameras/opencv/camera_opencv.py @@ -0,0 +1,482 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Provides the OpenCVCamera class for capturing frames from cameras using OpenCV. +""" + +import logging +import math +import platform +import time +from pathlib import Path +from threading import Event, Lock, Thread +from typing import Any, Dict, List + +import cv2 +import numpy as np + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..camera import Camera +from ..utils import get_cv2_backend, get_cv2_rotation +from .configuration_opencv import ColorMode, OpenCVCameraConfig + +# NOTE(Steven): The maximum opencv device index depends on your operating system. For instance, +# if you have 3 cameras, they should be associated to index 0, 1, and 2. This is the case +# on MacOS. However, on Ubuntu, the indices are different like 6, 16, 23. +# When you change the USB port or reboot the computer, the operating system might +# treat the same cameras as new devices. Thus we select a higher bound to search indices. +MAX_OPENCV_INDEX = 60 + +logger = logging.getLogger(__name__) + + +class OpenCVCamera(Camera): + """ + Manages camera interactions using OpenCV for efficient frame recording. + + This class provides a high-level interface to connect to, configure, and read + frames from cameras compatible with OpenCV's VideoCapture. It supports both + synchronous and asynchronous frame reading. + + An OpenCVCamera instance requires a camera index (e.g., 0) or a device path + (e.g., '/dev/video0' on Linux). Camera indices can be unstable across reboots + or port changes, especially on Linux. Use the provided utility script to find + available camera indices or paths: + ```bash + python -m lerobot.find_cameras opencv + ``` + + The camera's default settings (FPS, resolution, color mode) are used unless + overridden in the configuration. + + Example: + ```python + from lerobot.common.cameras.opencv import OpenCVCamera + from lerobot.common.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation + + # Basic usage with camera index 0 + config = OpenCVCameraConfig(index_or_path=0) + camera = OpenCVCamera(config) + camera.connect() + + # Read 1 frame synchronously + color_image = camera.read() + print(color_image.shape) + + # Read 1 frame asynchronously + async_image = camera.async_read() + + # When done, properly disconnect the camera using + camera.disconnect() + + # Example with custom settings + custom_config = OpenCVCameraConfig( + index_or_path='/dev/video0', # Or use an index + fps=30, + width=1280, + height=720, + color_mode=ColorMode.RGB, + rotation=Cv2Rotation.ROTATE_90 + ) + custom_camera = OpenCVCamera(custom_config) + # ... connect, read, disconnect ... + ``` + """ + + def __init__(self, config: OpenCVCameraConfig): + """ + Initializes the OpenCVCamera instance. + + Args: + config: The configuration settings for the camera. + """ + super().__init__(config) + + self.config = config + self.index_or_path = config.index_or_path + + self.fps = config.fps + self.color_mode = config.color_mode + self.warmup_s = config.warmup_s + + self.videocapture: cv2.VideoCapture | None = None + + self.thread: Thread | None = None + self.stop_event: Event | None = None + self.frame_lock: Lock = Lock() + self.latest_frame: np.ndarray | None = None + self.new_frame_event: Event = Event() + + self.rotation: int | None = get_cv2_rotation(config.rotation) + self.backend: int = get_cv2_backend() + + if self.height and self.width: + self.capture_width, self.capture_height = self.width, self.height + if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]: + self.capture_width, self.capture_height = self.height, self.width + + def __str__(self) -> str: + return f"{self.__class__.__name__}({self.index_or_path})" + + @property + def is_connected(self) -> bool: + """Checks if the camera is currently connected and opened.""" + return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened() + + def connect(self, warmup: bool = True): + """ + Connects to the OpenCV camera specified in the configuration. + + Initializes the OpenCV VideoCapture object, sets desired camera properties + (FPS, width, height), and performs initial checks. + + Raises: + DeviceAlreadyConnectedError: If the camera is already connected. + ConnectionError: If the specified camera index/path is not found or the camera is found but fails to open. + RuntimeError: If the camera opens but fails to apply requested FPS/resolution settings. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} is already connected.") + + # Use 1 thread for OpenCV operations to avoid potential conflicts or + # blocking in multi-threaded applications, especially during data collection. + cv2.setNumThreads(1) + + self.videocapture = cv2.VideoCapture(self.index_or_path, self.backend) + + if not self.videocapture.isOpened(): + self.videocapture.release() + self.videocapture = None + raise ConnectionError( + f"Failed to open {self}." + f"Run `python -m lerobot.find_cameras opencv` to find available cameras." + ) + + self._configure_capture_settings() + + if warmup: + start_time = time.time() + while time.time() - start_time < self.warmup_s: + self.read() + time.sleep(0.1) + + logger.info(f"{self} connected.") + + def _configure_capture_settings(self) -> None: + """ + Applies the specified FPS, width, and height settings to the connected camera. + + This method attempts to set the camera properties via OpenCV. It checks if + the camera successfully applied the settings and raises an error if not. + + Args: + fps: The desired frames per second. If None, the setting is skipped. + width: The desired capture width. If None, the setting is skipped. + height: The desired capture height. If None, the setting is skipped. + + Raises: + RuntimeError: If the camera fails to set any of the specified properties + to the requested value. + DeviceNotConnectedError: If the camera is not connected when attempting + to configure settings. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.") + + if self.fps is None: + self.fps = self.videocapture.get(cv2.CAP_PROP_FPS) + else: + self._validate_fps() + + default_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH))) + default_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT))) + + if self.width is None or self.height is None: + self.width, self.height = default_width, default_height + self.capture_width, self.capture_height = default_width, default_height + if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]: + self.width, self.height = default_height, default_width + self.capture_width, self.capture_height = default_width, default_height + else: + self._validate_width_and_height() + + def _validate_fps(self) -> None: + """Validates and sets the camera's frames per second (FPS).""" + + success = self.videocapture.set(cv2.CAP_PROP_FPS, float(self.fps)) + actual_fps = self.videocapture.get(cv2.CAP_PROP_FPS) + # Use math.isclose for robust float comparison + if not success or not math.isclose(self.fps, actual_fps, rel_tol=1e-3): + raise RuntimeError(f"{self} failed to set fps={self.fps} ({actual_fps=}).") + + def _validate_width_and_height(self) -> None: + """Validates and sets the camera's frame capture width and height.""" + + width_success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width)) + height_success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height)) + + actual_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH))) + if not width_success or self.capture_width != actual_width: + raise RuntimeError( + f"{self} failed to set capture_width={self.capture_width} ({actual_width=}, {width_success=})." + ) + + actual_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT))) + if not height_success or self.capture_height != actual_height: + raise RuntimeError( + f"{self} failed to set capture_height={self.capture_height} ({actual_height=}, {height_success=})." + ) + + @staticmethod + def find_cameras() -> List[Dict[str, Any]]: + """ + Detects available OpenCV cameras connected to the system. + + On Linux, it scans '/dev/video*' paths. On other systems (like macOS, Windows), + it checks indices from 0 up to `MAX_OPENCV_INDEX`. + + Returns: + List[Dict[str, Any]]: A list of dictionaries, + where each dictionary contains 'type', 'id' (port index or path), + and the default profile properties (width, height, fps, format). + """ + found_cameras_info = [] + + if platform.system() == "Linux": + possible_paths = sorted(Path("/dev").glob("video*"), key=lambda p: p.name) + targets_to_scan = [str(p) for p in possible_paths] + else: + targets_to_scan = list(range(MAX_OPENCV_INDEX)) + + for target in targets_to_scan: + camera = cv2.VideoCapture(target) + if camera.isOpened(): + default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)) + default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT)) + default_fps = camera.get(cv2.CAP_PROP_FPS) + default_format = camera.get(cv2.CAP_PROP_FORMAT) + camera_info = { + "name": f"OpenCV Camera @ {target}", + "type": "OpenCV", + "id": target, + "backend_api": camera.getBackendName(), + "default_stream_profile": { + "format": default_format, + "width": default_width, + "height": default_height, + "fps": default_fps, + }, + } + + found_cameras_info.append(camera_info) + camera.release() + + return found_cameras_info + + def read(self, color_mode: ColorMode | None = None) -> np.ndarray: + """ + Reads a single frame synchronously from the camera. + + This is a blocking call. It waits for the next available frame from the + camera hardware via OpenCV. + + Args: + color_mode (Optional[ColorMode]): If specified, overrides the default + color mode (`self.color_mode`) for this read operation (e.g., + request RGB even if default is BGR). + + Returns: + np.ndarray: The captured frame as a NumPy array in the format + (height, width, channels), using the specified or default + color mode and applying any configured rotation. + + Raises: + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If reading the frame from the camera fails or if the + received frame dimensions don't match expectations before rotation. + ValueError: If an invalid `color_mode` is requested. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + start_time = time.perf_counter() + + ret, frame = self.videocapture.read() + + if not ret or frame is None: + raise RuntimeError(f"{self} read failed (status={ret}).") + + processed_frame = self._postprocess_image(frame, color_mode) + + read_duration_ms = (time.perf_counter() - start_time) * 1e3 + logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") + + return processed_frame + + def _postprocess_image(self, image: np.ndarray, color_mode: ColorMode | None = None) -> np.ndarray: + """ + Applies color conversion, dimension validation, and rotation to a raw frame. + + Args: + image (np.ndarray): The raw image frame (expected BGR format from OpenCV). + color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None, + uses the instance's default `self.color_mode`. + + Returns: + np.ndarray: The processed image frame. + + Raises: + ValueError: If the requested `color_mode` is invalid. + RuntimeError: If the raw frame dimensions do not match the configured + `width` and `height`. + """ + requested_color_mode = self.color_mode if color_mode is None else color_mode + + if requested_color_mode not in (ColorMode.RGB, ColorMode.BGR): + raise ValueError( + f"Invalid color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." + ) + + h, w, c = image.shape + + if h != self.capture_height or w != self.capture_width: + raise RuntimeError( + f"{self} frame width={w} or height={h} do not match configured width={self.capture_width} or height={self.capture_height}." + ) + + if c != 3: + raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).") + + processed_image = image + if requested_color_mode == ColorMode.RGB: + processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]: + processed_image = cv2.rotate(processed_image, self.rotation) + + return processed_image + + def _read_loop(self): + """ + Internal loop run by the background thread for asynchronous reading. + + On each iteration: + 1. Reads a color frame + 2. Stores result in latest_frame (thread-safe) + 3. Sets new_frame_event to notify listeners + + Stops on DeviceNotConnectedError, logs other errors and continues. + """ + while not self.stop_event.is_set(): + try: + color_image = self.read() + + with self.frame_lock: + self.latest_frame = color_image + self.new_frame_event.set() + + except DeviceNotConnectedError: + break + except Exception as e: + logger.warning(f"Error reading frame in background thread for {self}: {e}") + + def _start_read_thread(self) -> None: + """Starts or restarts the background read thread if it's not running.""" + if self.thread is not None and self.thread.is_alive(): + self.thread.join(timeout=0.1) + if self.stop_event is not None: + self.stop_event.set() + + self.stop_event = Event() + self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop") + self.thread.daemon = True + self.thread.start() + + def _stop_read_thread(self) -> None: + """Signals the background read thread to stop and waits for it to join.""" + if self.stop_event is not None: + self.stop_event.set() + + if self.thread is not None and self.thread.is_alive(): + self.thread.join(timeout=2.0) + + self.thread = None + self.stop_event = None + + def async_read(self, timeout_ms: float = 200) -> np.ndarray: + """ + Reads the latest available frame asynchronously. + + This method retrieves the most recent frame captured by the background + read thread. It does not block waiting for the camera hardware directly, + but may wait up to timeout_ms for the background thread to provide a frame. + + Args: + timeout_ms (float): Maximum time in milliseconds to wait for a frame + to become available. Defaults to 200ms (0.2 seconds). + + Returns: + np.ndarray: The latest captured frame as a NumPy array in the format + (height, width, channels), processed according to configuration. + + Raises: + DeviceNotConnectedError: If the camera is not connected. + TimeoutError: If no frame becomes available within the specified timeout. + RuntimeError: If an unexpected error occurs. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.thread is None or not self.thread.is_alive(): + self._start_read_thread() + + if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0): + thread_alive = self.thread is not None and self.thread.is_alive() + raise TimeoutError( + f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. " + f"Read thread alive: {thread_alive}." + ) + + with self.frame_lock: + frame = self.latest_frame + self.new_frame_event.clear() + + if frame is None: + raise RuntimeError(f"Internal error: Event set but no frame available for {self}.") + + return frame + + def disconnect(self): + """ + Disconnects from the camera and cleans up resources. + + Stops the background read thread (if running) and releases the OpenCV + VideoCapture object. + + Raises: + DeviceNotConnectedError: If the camera is already disconnected. + """ + if not self.is_connected and self.thread is None: + raise DeviceNotConnectedError(f"{self} not connected.") + + if self.thread is not None: + self._stop_read_thread() + + if self.videocapture is not None: + self.videocapture.release() + self.videocapture = None + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/cameras/opencv/configuration_opencv.py b/lerobot/common/cameras/opencv/configuration_opencv.py new file mode 100644 index 0000000000000000000000000000000000000000..25d1a91ddc8c9db0c9dbe53f0742e42e6f9889f4 --- /dev/null +++ b/lerobot/common/cameras/opencv/configuration_opencv.py @@ -0,0 +1,73 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from pathlib import Path + +from ..configs import CameraConfig, ColorMode, Cv2Rotation + + +@CameraConfig.register_subclass("opencv") +@dataclass +class OpenCVCameraConfig(CameraConfig): + """Configuration class for OpenCV-based camera devices or video files. + + This class provides configuration options for cameras accessed through OpenCV, + supporting both physical camera devices and video files. It includes settings + for resolution, frame rate, color mode, and image rotation. + + Example configurations: + ```python + # Basic configurations + OpenCVCameraConfig(0, 30, 1280, 720) # 1280x720 @ 30FPS + OpenCVCameraConfig(/dev/video4, 60, 640, 480) # 640x480 @ 60FPS + + # Advanced configurations + OpenCVCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90) # With 90° rotation + ``` + + Attributes: + index_or_path: Either an integer representing the camera device index, + or a Path object pointing to a video file. + fps: Requested frames per second for the color stream. + width: Requested frame width in pixels for the color stream. + height: Requested frame height in pixels for the color stream. + color_mode: Color mode for image output (RGB or BGR). Defaults to RGB. + rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation. + warmup_s: Time reading frames before returning from connect (in seconds) + + Note: + - Only 3-channel color output (RGB/BGR) is currently supported. + """ + + index_or_path: int | Path + color_mode: ColorMode = ColorMode.RGB + rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION + warmup_s: int = 1 + + def __post_init__(self): + if self.color_mode not in (ColorMode.RGB, ColorMode.BGR): + raise ValueError( + f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided." + ) + + if self.rotation not in ( + Cv2Rotation.NO_ROTATION, + Cv2Rotation.ROTATE_90, + Cv2Rotation.ROTATE_180, + Cv2Rotation.ROTATE_270, + ): + raise ValueError( + f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided." + ) diff --git a/lerobot/common/cameras/realsense/__init__.py b/lerobot/common/cameras/realsense/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc5184a99bc33c17d0c759dc5a561ce800f5a278 --- /dev/null +++ b/lerobot/common/cameras/realsense/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .camera_realsense import RealSenseCamera +from .configuration_realsense import RealSenseCameraConfig diff --git a/lerobot/common/cameras/realsense/camera_realsense.py b/lerobot/common/cameras/realsense/camera_realsense.py new file mode 100644 index 0000000000000000000000000000000000000000..0bb8f918d2a71e5cc1a1cec77d1ab93823ba721f --- /dev/null +++ b/lerobot/common/cameras/realsense/camera_realsense.py @@ -0,0 +1,556 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Provides the RealSenseCamera class for capturing frames from Intel RealSense cameras. +""" + +import logging +import time +from threading import Event, Lock, Thread +from typing import Any, Dict, List + +import cv2 +import numpy as np + +try: + import pyrealsense2 as rs +except Exception as e: + logging.info(f"Could not import realsense: {e}") + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..camera import Camera +from ..configs import ColorMode +from ..utils import get_cv2_rotation +from .configuration_realsense import RealSenseCameraConfig + +logger = logging.getLogger(__name__) + + +class RealSenseCamera(Camera): + """ + Manages interactions with Intel RealSense cameras for frame and depth recording. + + This class provides an interface similar to `OpenCVCamera` but tailored for + RealSense devices, leveraging the `pyrealsense2` library. It uses the camera's + unique serial number for identification, offering more stability than device + indices, especially on Linux. It also supports capturing depth maps alongside + color frames. + + Use the provided utility script to find available camera indices and default profiles: + ```bash + python -m lerobot.find_cameras realsense + ``` + + A `RealSenseCamera` instance requires a configuration object specifying the + camera's serial number or a unique device name. If using the name, ensure only + one camera with that name is connected. + + The camera's default settings (FPS, resolution, color mode) from the stream + profile are used unless overridden in the configuration. + + Example: + ```python + from lerobot.common.cameras.realsense import RealSenseCamera, RealSenseCameraConfig + from lerobot.common.cameras import ColorMode, Cv2Rotation + + # Basic usage with serial number + config = RealSenseCameraConfig(serial_number_or_name="0123456789") # Replace with actual SN + camera = RealSenseCamera(config) + camera.connect() + + # Read 1 frame synchronously + color_image = camera.read() + print(color_image.shape) + + # Read 1 frame asynchronously + async_image = camera.async_read() + + # When done, properly disconnect the camera using + camera.disconnect() + + # Example with depth capture and custom settings + custom_config = RealSenseCameraConfig( + serial_number_or_name="0123456789", # Replace with actual SN + fps=30, + width=1280, + height=720, + color_mode=ColorMode.BGR, # Request BGR output + rotation=Cv2Rotation.NO_ROTATION, + use_depth=True + ) + depth_camera = RealSenseCamera(custom_config) + depth_camera.connect() + + # Read 1 depth frame + depth_map = depth_camera.read_depth() + + # Example using a unique camera name + name_config = RealSenseCameraConfig(serial_number_or_name="Intel RealSense D435") # If unique + name_camera = RealSenseCamera(name_config) + # ... connect, read, disconnect ... + ``` + """ + + def __init__(self, config: RealSenseCameraConfig): + """ + Initializes the RealSenseCamera instance. + + Args: + config: The configuration settings for the camera. + """ + + super().__init__(config) + + self.config = config + + if config.serial_number_or_name.isdigit(): + self.serial_number = config.serial_number_or_name + else: + self.serial_number = self._find_serial_number_from_name(config.serial_number_or_name) + + self.fps = config.fps + self.color_mode = config.color_mode + self.use_depth = config.use_depth + self.warmup_s = config.warmup_s + + self.rs_pipeline: rs.pipeline | None = None + self.rs_profile: rs.pipeline_profile | None = None + + self.thread: Thread | None = None + self.stop_event: Event | None = None + self.frame_lock: Lock = Lock() + self.latest_frame: np.ndarray | None = None + self.new_frame_event: Event = Event() + + self.rotation: int | None = get_cv2_rotation(config.rotation) + + if self.height and self.width: + self.capture_width, self.capture_height = self.width, self.height + if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]: + self.capture_width, self.capture_height = self.height, self.width + + def __str__(self) -> str: + return f"{self.__class__.__name__}({self.serial_number})" + + @property + def is_connected(self) -> bool: + """Checks if the camera pipeline is started and streams are active.""" + return self.rs_pipeline is not None and self.rs_profile is not None + + def connect(self, warmup: bool = True): + """ + Connects to the RealSense camera specified in the configuration. + + Initializes the RealSense pipeline, configures the required streams (color + and optionally depth), starts the pipeline, and validates the actual stream settings. + + Raises: + DeviceAlreadyConnectedError: If the camera is already connected. + ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique). + ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all. + RuntimeError: If the pipeline starts but fails to apply requested settings. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} is already connected.") + + self.rs_pipeline = rs.pipeline() + rs_config = rs.config() + self._configure_rs_pipeline_config(rs_config) + + try: + self.rs_profile = self.rs_pipeline.start(rs_config) + except RuntimeError as e: + self.rs_profile = None + self.rs_pipeline = None + raise ConnectionError( + f"Failed to open {self}." + "Run `python -m lerobot.find_cameras realsense` to find available cameras." + ) from e + + self._configure_capture_settings() + + if warmup: + time.sleep( + 1 + ) # NOTE(Steven): RS cameras need a bit of time to warm up before the first read. If we don't wait, the first read from the warmup will raise. + start_time = time.time() + while time.time() - start_time < self.warmup_s: + self.read() + time.sleep(0.1) + + logger.info(f"{self} connected.") + + @staticmethod + def find_cameras() -> List[Dict[str, Any]]: + """ + Detects available Intel RealSense cameras connected to the system. + + Returns: + List[Dict[str, Any]]: A list of dictionaries, + where each dictionary contains 'type', 'id' (serial number), 'name', + firmware version, USB type, and other available specs, and the default profile properties (width, height, fps, format). + + Raises: + OSError: If pyrealsense2 is not installed. + ImportError: If pyrealsense2 is not installed. + """ + found_cameras_info = [] + context = rs.context() + devices = context.query_devices() + + for device in devices: + camera_info = { + "name": device.get_info(rs.camera_info.name), + "type": "RealSense", + "id": device.get_info(rs.camera_info.serial_number), + "firmware_version": device.get_info(rs.camera_info.firmware_version), + "usb_type_descriptor": device.get_info(rs.camera_info.usb_type_descriptor), + "physical_port": device.get_info(rs.camera_info.physical_port), + "product_id": device.get_info(rs.camera_info.product_id), + "product_line": device.get_info(rs.camera_info.product_line), + } + + # Get stream profiles for each sensor + sensors = device.query_sensors() + for sensor in sensors: + profiles = sensor.get_stream_profiles() + + for profile in profiles: + if profile.is_video_stream_profile() and profile.is_default(): + vprofile = profile.as_video_stream_profile() + stream_info = { + "stream_type": vprofile.stream_name(), + "format": vprofile.format().name, + "width": vprofile.width(), + "height": vprofile.height(), + "fps": vprofile.fps(), + } + camera_info["default_stream_profile"] = stream_info + + found_cameras_info.append(camera_info) + + return found_cameras_info + + def _find_serial_number_from_name(self, name: str) -> str: + """Finds the serial number for a given unique camera name.""" + camera_infos = self.find_cameras() + found_devices = [cam for cam in camera_infos if str(cam["name"]) == name] + + if not found_devices: + available_names = [cam["name"] for cam in camera_infos] + raise ValueError( + f"No RealSense camera found with name '{name}'. Available camera names: {available_names}" + ) + + if len(found_devices) > 1: + serial_numbers = [dev["serial_number"] for dev in found_devices] + raise ValueError( + f"Multiple RealSense cameras found with name '{name}'. " + f"Please use a unique serial number instead. Found SNs: {serial_numbers}" + ) + + serial_number = str(found_devices[0]["serial_number"]) + return serial_number + + def _configure_rs_pipeline_config(self, rs_config): + """Creates and configures the RealSense pipeline configuration object.""" + rs.config.enable_device(rs_config, self.serial_number) + + if self.width and self.height and self.fps: + rs_config.enable_stream( + rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps + ) + if self.use_depth: + rs_config.enable_stream( + rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps + ) + else: + rs_config.enable_stream(rs.stream.color) + if self.use_depth: + rs_config.enable_stream(rs.stream.depth) + + def _configure_capture_settings(self) -> None: + """Sets fps, width, and height from device stream if not already configured. + + Uses the color stream profile to update unset attributes. Handles rotation by + swapping width/height when needed. Original capture dimensions are always stored. + + Raises: + DeviceNotConnectedError: If device is not connected. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.") + + stream = self.rs_profile.get_stream(rs.stream.color).as_video_stream_profile() + + if self.fps is None: + self.fps = stream.fps() + + if self.width is None or self.height is None: + actual_width = int(round(stream.width())) + actual_height = int(round(stream.height())) + if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]: + self.width, self.height = actual_height, actual_width + self.capture_width, self.capture_height = actual_width, actual_height + else: + self.width, self.height = actual_width, actual_height + self.capture_width, self.capture_height = actual_width, actual_height + + def read_depth(self, timeout_ms: int = 200) -> np.ndarray: + """ + Reads a single frame (depth) synchronously from the camera. + + This is a blocking call. It waits for a coherent set of frames (depth) + from the camera hardware via the RealSense pipeline. + + Args: + timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms. + + Returns: + np.ndarray: The depth map as a NumPy array (height, width) + of type `np.uint16` (raw depth values in millimeters) and rotation. + + Raises: + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If reading frames from the pipeline fails or frames are invalid. + """ + + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + if not self.use_depth: + raise RuntimeError( + f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}." + ) + + start_time = time.perf_counter() + + ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms) + + if not ret or frame is None: + raise RuntimeError(f"{self} read_depth failed (status={ret}).") + + depth_frame = frame.get_depth_frame() + depth_map = np.asanyarray(depth_frame.get_data()) + + depth_map_processed = self._postprocess_image(depth_map, depth_frame=True) + + read_duration_ms = (time.perf_counter() - start_time) * 1e3 + logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") + + return depth_map_processed + + def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> np.ndarray: + """ + Reads a single frame (color) synchronously from the camera. + + This is a blocking call. It waits for a coherent set of frames (color) + from the camera hardware via the RealSense pipeline. + + Args: + timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms. + + Returns: + np.ndarray: The captured color frame as a NumPy array + (height, width, channels), processed according to `color_mode` and rotation. + + Raises: + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If reading frames from the pipeline fails or frames are invalid. + ValueError: If an invalid `color_mode` is requested. + """ + + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + start_time = time.perf_counter() + + ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms) + + if not ret or frame is None: + raise RuntimeError(f"{self} read failed (status={ret}).") + + color_frame = frame.get_color_frame() + color_image_raw = np.asanyarray(color_frame.get_data()) + + color_image_processed = self._postprocess_image(color_image_raw, color_mode) + + read_duration_ms = (time.perf_counter() - start_time) * 1e3 + logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") + + return color_image_processed + + def _postprocess_image( + self, image: np.ndarray, color_mode: ColorMode | None = None, depth_frame: bool = False + ) -> np.ndarray: + """ + Applies color conversion, dimension validation, and rotation to a raw color frame. + + Args: + image (np.ndarray): The raw image frame (expected RGB format from RealSense). + color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None, + uses the instance's default `self.color_mode`. + + Returns: + np.ndarray: The processed image frame according to `self.color_mode` and `self.rotation`. + + Raises: + ValueError: If the requested `color_mode` is invalid. + RuntimeError: If the raw frame dimensions do not match the configured + `width` and `height`. + """ + + if color_mode and color_mode not in (ColorMode.RGB, ColorMode.BGR): + raise ValueError( + f"Invalid requested color mode '{color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." + ) + + if depth_frame: + h, w = image.shape + else: + h, w, c = image.shape + + if c != 3: + raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).") + + if h != self.capture_height or w != self.capture_width: + raise RuntimeError( + f"{self} frame width={w} or height={h} do not match configured width={self.capture_width} or height={self.capture_height}." + ) + + processed_image = image + if self.color_mode == ColorMode.BGR: + processed_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]: + processed_image = cv2.rotate(processed_image, self.rotation) + + return processed_image + + def _read_loop(self): + """ + Internal loop run by the background thread for asynchronous reading. + + On each iteration: + 1. Reads a color frame with 500ms timeout + 2. Stores result in latest_frame (thread-safe) + 3. Sets new_frame_event to notify listeners + + Stops on DeviceNotConnectedError, logs other errors and continues. + """ + while not self.stop_event.is_set(): + try: + color_image = self.read(timeout_ms=500) + + with self.frame_lock: + self.latest_frame = color_image + self.new_frame_event.set() + + except DeviceNotConnectedError: + break + except Exception as e: + logger.warning(f"Error reading frame in background thread for {self}: {e}") + + def _start_read_thread(self) -> None: + """Starts or restarts the background read thread if it's not running.""" + if self.thread is not None and self.thread.is_alive(): + self.thread.join(timeout=0.1) + if self.stop_event is not None: + self.stop_event.set() + + self.stop_event = Event() + self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop") + self.thread.daemon = True + self.thread.start() + + def _stop_read_thread(self): + """Signals the background read thread to stop and waits for it to join.""" + if self.stop_event is not None: + self.stop_event.set() + + if self.thread is not None and self.thread.is_alive(): + self.thread.join(timeout=2.0) + + self.thread = None + self.stop_event = None + + # NOTE(Steven): Missing implementation for depth for now + def async_read(self, timeout_ms: float = 200) -> np.ndarray: + """ + Reads the latest available frame data (color) asynchronously. + + This method retrieves the most recent color frame captured by the background + read thread. It does not block waiting for the camera hardware directly, + but may wait up to timeout_ms for the background thread to provide a frame. + + Args: + timeout_ms (float): Maximum time in milliseconds to wait for a frame + to become available. Defaults to 200ms (0.2 seconds). + + Returns: + np.ndarray: + The latest captured frame data (color image), processed according to configuration. + + Raises: + DeviceNotConnectedError: If the camera is not connected. + TimeoutError: If no frame data becomes available within the specified timeout. + RuntimeError: If the background thread died unexpectedly or another error occurs. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.thread is None or not self.thread.is_alive(): + self._start_read_thread() + + if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0): + thread_alive = self.thread is not None and self.thread.is_alive() + raise TimeoutError( + f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. " + f"Read thread alive: {thread_alive}." + ) + + with self.frame_lock: + frame = self.latest_frame + self.new_frame_event.clear() + + if frame is None: + raise RuntimeError(f"Internal error: Event set but no frame available for {self}.") + + return frame + + def disconnect(self): + """ + Disconnects from the camera, stops the pipeline, and cleans up resources. + + Stops the background read thread (if running) and stops the RealSense pipeline. + + Raises: + DeviceNotConnectedError: If the camera is already disconnected (pipeline not running). + """ + + if not self.is_connected and self.thread is None: + raise DeviceNotConnectedError( + f"Attempted to disconnect {self}, but it appears already disconnected." + ) + + if self.thread is not None: + self._stop_read_thread() + + if self.rs_pipeline is not None: + self.rs_pipeline.stop() + self.rs_pipeline = None + self.rs_profile = None + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/cameras/realsense/configuration_realsense.py b/lerobot/common/cameras/realsense/configuration_realsense.py new file mode 100644 index 0000000000000000000000000000000000000000..9f4aa6bc4c665d5a69f325522dc5928feabf66c7 --- /dev/null +++ b/lerobot/common/cameras/realsense/configuration_realsense.py @@ -0,0 +1,82 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..configs import CameraConfig, ColorMode, Cv2Rotation + + +@CameraConfig.register_subclass("intelrealsense") +@dataclass +class RealSenseCameraConfig(CameraConfig): + """Configuration class for Intel RealSense cameras. + + This class provides specialized configuration options for Intel RealSense cameras, + including support for depth sensing and device identification via serial number or name. + + Example configurations for Intel RealSense D405: + ```python + # Basic configurations + RealSenseCameraConfig("0123456789", 30, 1280, 720) # 1280x720 @ 30FPS + RealSenseCameraConfig("0123456789", 60, 640, 480) # 640x480 @ 60FPS + + # Advanced configurations + RealSenseCameraConfig("0123456789", 30, 640, 480, use_depth=True) # With depth sensing + RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90) # With 90° rotation + ``` + + Attributes: + fps: Requested frames per second for the color stream. + width: Requested frame width in pixels for the color stream. + height: Requested frame height in pixels for the color stream. + serial_number_or_name: Unique serial number or human-readable name to identify the camera. + color_mode: Color mode for image output (RGB or BGR). Defaults to RGB. + use_depth: Whether to enable depth stream. Defaults to False. + rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation. + warmup_s: Time reading frames before returning from connect (in seconds) + + Note: + - Either name or serial_number must be specified. + - Depth stream configuration (if enabled) will use the same FPS as the color stream. + - The actual resolution and FPS may be adjusted by the camera to the nearest supported mode. + - For `fps`, `width` and `height`, either all of them need to be set, or none of them. + """ + + serial_number_or_name: str + color_mode: ColorMode = ColorMode.RGB + use_depth: bool = False + rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION + warmup_s: int = 1 + + def __post_init__(self): + if self.color_mode not in (ColorMode.RGB, ColorMode.BGR): + raise ValueError( + f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided." + ) + + if self.rotation not in ( + Cv2Rotation.NO_ROTATION, + Cv2Rotation.ROTATE_90, + Cv2Rotation.ROTATE_180, + Cv2Rotation.ROTATE_270, + ): + raise ValueError( + f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided." + ) + + values = (self.fps, self.width, self.height) + if any(v is not None for v in values) and any(v is None for v in values): + raise ValueError( + "For `fps`, `width` and `height`, either all of them need to be set, or none of them." + ) diff --git a/lerobot/common/cameras/utils.py b/lerobot/common/cameras/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d764060bc395cfd627aed42ce270cbd63e4d6430 --- /dev/null +++ b/lerobot/common/cameras/utils.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import platform +from pathlib import Path +from typing import TypeAlias + +from .camera import Camera +from .configs import CameraConfig, Cv2Rotation + +IndexOrPath: TypeAlias = int | Path + + +def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]: + cameras = {} + + for key, cfg in camera_configs.items(): + if cfg.type == "opencv": + from .opencv import OpenCVCamera + + cameras[key] = OpenCVCamera(cfg) + + elif cfg.type == "intelrealsense": + from .realsense.camera_realsense import RealSenseCamera + + cameras[key] = RealSenseCamera(cfg) + else: + raise ValueError(f"The motor type '{cfg.type}' is not valid.") + + return cameras + + +def get_cv2_rotation(rotation: Cv2Rotation) -> int | None: + import cv2 + + if rotation == Cv2Rotation.ROTATE_90: + return cv2.ROTATE_90_CLOCKWISE + elif rotation == Cv2Rotation.ROTATE_180: + return cv2.ROTATE_180 + elif rotation == Cv2Rotation.ROTATE_270: + return cv2.ROTATE_90_COUNTERCLOCKWISE + else: + return None + + +def get_cv2_backend() -> int: + import cv2 + + if platform.system() == "Windows": + return cv2.CAP_AVFOUNDATION + else: + return cv2.CAP_ANY diff --git a/lerobot/common/constants.py b/lerobot/common/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..cd44fe04eafcd65fa101c3d6120dcb3b111e863c --- /dev/null +++ b/lerobot/common/constants.py @@ -0,0 +1,53 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# keys +import os +from pathlib import Path + +from huggingface_hub.constants import HF_HOME + +OBS_ENV_STATE = "observation.environment_state" +OBS_STATE = "observation.state" +OBS_IMAGE = "observation.image" +OBS_IMAGES = "observation.images" +ACTION = "action" +REWARD = "next.reward" + +ROBOTS = "robots" +TELEOPERATORS = "teleoperators" + +# files & directories +CHECKPOINTS_DIR = "checkpoints" +LAST_CHECKPOINT_LINK = "last" +PRETRAINED_MODEL_DIR = "pretrained_model" +TRAINING_STATE_DIR = "training_state" +RNG_STATE = "rng_state.safetensors" +TRAINING_STEP = "training_step.json" +OPTIMIZER_STATE = "optimizer_state.safetensors" +OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json" +SCHEDULER_STATE = "scheduler_state.json" + +if "LEROBOT_HOME" in os.environ: + raise ValueError( + f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n" + "'LEROBOT_HOME' is deprecated, please use 'HF_LEROBOT_HOME' instead." + ) + +# cache dir +default_cache_path = Path(HF_HOME) / "lerobot" +HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser() + +# calibration dir +default_calibration_path = HF_LEROBOT_HOME / "calibration" +HF_LEROBOT_CALIBRATION = Path(os.getenv("HF_LEROBOT_CALIBRATION", default_calibration_path)).expanduser() diff --git a/lerobot/common/datasets/backward_compatibility.py b/lerobot/common/datasets/backward_compatibility.py new file mode 100644 index 0000000000000000000000000000000000000000..a51191d6ceac3c783ef5951be31d11d54fe203fa --- /dev/null +++ b/lerobot/common/datasets/backward_compatibility.py @@ -0,0 +1,68 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import packaging.version + +V2_MESSAGE = """ +The dataset you requested ({repo_id}) is in {version} format. + +We introduced a new format since v2.0 which is not backward compatible with v1.x. +Please, use our conversion script. Modify the following command with your own task description: +``` +python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \\ + --repo-id {repo_id} \\ + --single-task "TASK DESCRIPTION." # <---- /!\\ Replace TASK DESCRIPTION /!\\ +``` + +A few examples to replace TASK DESCRIPTION: "Pick up the blue cube and place it into the bin.", "Insert the +peg into the socket.", "Slide open the ziploc bag.", "Take the elevator to the 1st floor.", "Open the top +cabinet, store the pot inside it then close the cabinet.", "Push the T-shaped block onto the T-shaped +target.", "Grab the spray paint on the shelf and place it in the bin on top of the robot dog.", "Fold the +sweatshirt.", ... + +If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb) +or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose). +""" + +V21_MESSAGE = """ +The dataset you requested ({repo_id}) is in {version} format. +While current version of LeRobot is backward-compatible with it, the version of your dataset still uses global +stats instead of per-episode stats. Update your dataset stats to the new format using this command: +``` +python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py --repo-id={repo_id} +``` + +If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb) +or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose). +""" + +FUTURE_MESSAGE = """ +The dataset you requested ({repo_id}) is only available in {version} format. +As we cannot ensure forward compatibility with it, please update your current version of lerobot. +""" + + +class CompatibilityError(Exception): ... + + +class BackwardCompatibilityError(CompatibilityError): + def __init__(self, repo_id: str, version: packaging.version.Version): + message = V2_MESSAGE.format(repo_id=repo_id, version=version) + super().__init__(message) + + +class ForwardCompatibilityError(CompatibilityError): + def __init__(self, repo_id: str, version: packaging.version.Version): + message = FUTURE_MESSAGE.format(repo_id=repo_id, version=version) + super().__init__(message) diff --git a/lerobot/common/datasets/card_template.md b/lerobot/common/datasets/card_template.md new file mode 100644 index 0000000000000000000000000000000000000000..9f00a54d6245e66ff8cf4f4484f95838f8c452b9 --- /dev/null +++ b/lerobot/common/datasets/card_template.md @@ -0,0 +1,27 @@ +--- +# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 +# Doc / guide: https://huggingface.co/docs/hub/datasets-cards +{{ card_data }} +--- + +This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). + +## Dataset Description + +{{ dataset_description | default("", true) }} + +- **Homepage:** {{ url | default("[More Information Needed]", true)}} +- **Paper:** {{ paper | default("[More Information Needed]", true)}} +- **License:** {{ license | default("[More Information Needed]", true)}} + +## Dataset Structure + +{{ dataset_structure | default("[More Information Needed]", true)}} + +## Citation + +**BibTeX:** + +```bibtex +{{ citation_bibtex | default("[More Information Needed]", true)}} +``` diff --git a/lerobot/common/datasets/compute_stats.py b/lerobot/common/datasets/compute_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..c8f5a3020addbe4e694a3a1d4e4fd1d592271540 --- /dev/null +++ b/lerobot/common/datasets/compute_stats.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np + +from lerobot.common.datasets.utils import load_image_as_numpy + + +def estimate_num_samples( + dataset_len: int, min_num_samples: int = 100, max_num_samples: int = 10_000, power: float = 0.75 +) -> int: + """Heuristic to estimate the number of samples based on dataset size. + The power controls the sample growth relative to dataset size. + Lower the power for less number of samples. + + For default arguments, we have: + - from 1 to ~500, num_samples=100 + - at 1000, num_samples=177 + - at 2000, num_samples=299 + - at 5000, num_samples=594 + - at 10000, num_samples=1000 + - at 20000, num_samples=1681 + """ + if dataset_len < min_num_samples: + min_num_samples = dataset_len + return max(min_num_samples, min(int(dataset_len**power), max_num_samples)) + + +def sample_indices(data_len: int) -> list[int]: + num_samples = estimate_num_samples(data_len) + return np.round(np.linspace(0, data_len - 1, num_samples)).astype(int).tolist() + + +def auto_downsample_height_width(img: np.ndarray, target_size: int = 150, max_size_threshold: int = 300): + _, height, width = img.shape + + if max(width, height) < max_size_threshold: + # no downsampling needed + return img + + downsample_factor = int(width / target_size) if width > height else int(height / target_size) + return img[:, ::downsample_factor, ::downsample_factor] + + +def sample_images(image_paths: list[str]) -> np.ndarray: + sampled_indices = sample_indices(len(image_paths)) + + images = None + for i, idx in enumerate(sampled_indices): + path = image_paths[idx] + # we load as uint8 to reduce memory usage + img = load_image_as_numpy(path, dtype=np.uint8, channel_first=True) + img = auto_downsample_height_width(img) + + if images is None: + images = np.empty((len(sampled_indices), *img.shape), dtype=np.uint8) + + images[i] = img + + return images + + +def get_feature_stats(array: np.ndarray, axis: tuple, keepdims: bool) -> dict[str, np.ndarray]: + return { + "min": np.min(array, axis=axis, keepdims=keepdims), + "max": np.max(array, axis=axis, keepdims=keepdims), + "mean": np.mean(array, axis=axis, keepdims=keepdims), + "std": np.std(array, axis=axis, keepdims=keepdims), + "count": np.array([len(array)]), + } + + +def compute_episode_stats(episode_data: dict[str, list[str] | np.ndarray], features: dict) -> dict: + ep_stats = {} + for key, data in episode_data.items(): + if features[key]["dtype"] == "string": + continue # HACK: we should receive np.arrays of strings + elif features[key]["dtype"] in ["image", "video"]: + ep_ft_array = sample_images(data) # data is a list of image paths + axes_to_reduce = (0, 2, 3) # keep channel dim + keepdims = True + else: + ep_ft_array = data # data is already a np.ndarray + axes_to_reduce = 0 # compute stats over the first axis + keepdims = data.ndim == 1 # keep as np.array + + ep_stats[key] = get_feature_stats(ep_ft_array, axis=axes_to_reduce, keepdims=keepdims) + + # finally, we normalize and remove batch dim for images + if features[key]["dtype"] in ["image", "video"]: + ep_stats[key] = { + k: v if k == "count" else np.squeeze(v / 255.0, axis=0) for k, v in ep_stats[key].items() + } + + return ep_stats + + +def _assert_type_and_shape(stats_list: list[dict[str, dict]]): + for i in range(len(stats_list)): + for fkey in stats_list[i]: + for k, v in stats_list[i][fkey].items(): + if not isinstance(v, np.ndarray): + raise ValueError( + f"Stats must be composed of numpy array, but key '{k}' of feature '{fkey}' is of type '{type(v)}' instead." + ) + if v.ndim == 0: + raise ValueError("Number of dimensions must be at least 1, and is 0 instead.") + if k == "count" and v.shape != (1,): + raise ValueError(f"Shape of 'count' must be (1), but is {v.shape} instead.") + if "image" in fkey and k != "count" and v.shape != (3, 1, 1): + raise ValueError(f"Shape of '{k}' must be (3,1,1), but is {v.shape} instead.") + + +def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]: + """Aggregates stats for a single feature.""" + means = np.stack([s["mean"] for s in stats_ft_list]) + variances = np.stack([s["std"] ** 2 for s in stats_ft_list]) + counts = np.stack([s["count"] for s in stats_ft_list]) + total_count = counts.sum(axis=0) + + # Prepare weighted mean by matching number of dimensions + while counts.ndim < means.ndim: + counts = np.expand_dims(counts, axis=-1) + + # Compute the weighted mean + weighted_means = means * counts + total_mean = weighted_means.sum(axis=0) / total_count + + # Compute the variance using the parallel algorithm + delta_means = means - total_mean + weighted_variances = (variances + delta_means**2) * counts + total_variance = weighted_variances.sum(axis=0) / total_count + + return { + "min": np.min(np.stack([s["min"] for s in stats_ft_list]), axis=0), + "max": np.max(np.stack([s["max"] for s in stats_ft_list]), axis=0), + "mean": total_mean, + "std": np.sqrt(total_variance), + "count": total_count, + } + + +def aggregate_stats(stats_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]: + """Aggregate stats from multiple compute_stats outputs into a single set of stats. + + The final stats will have the union of all data keys from each of the stats dicts. + + For instance: + - new_min = min(min_dataset_0, min_dataset_1, ...) + - new_max = max(max_dataset_0, max_dataset_1, ...) + - new_mean = (mean of all data, weighted by counts) + - new_std = (std of all data) + """ + + _assert_type_and_shape(stats_list) + + data_keys = {key for stats in stats_list for key in stats} + aggregated_stats = {key: {} for key in data_keys} + + for key in data_keys: + stats_with_key = [stats[key] for stats in stats_list if key in stats] + aggregated_stats[key] = aggregate_feature_stats(stats_with_key) + + return aggregated_stats diff --git a/lerobot/common/datasets/factory.py b/lerobot/common/datasets/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..1d80c5a564d126d84ec0568320959cc3203f092e --- /dev/null +++ b/lerobot/common/datasets/factory.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +from pprint import pformat + +import torch + +from lerobot.common.datasets.lerobot_dataset import ( + LeRobotDataset, + LeRobotDatasetMetadata, + MultiLeRobotDataset, +) +from lerobot.common.datasets.transforms import ImageTransforms +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.train import TrainPipelineConfig + +IMAGENET_STATS = { + "mean": [[[0.485]], [[0.456]], [[0.406]]], # (c,1,1) + "std": [[[0.229]], [[0.224]], [[0.225]]], # (c,1,1) +} + + +def resolve_delta_timestamps( + cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata +) -> dict[str, list] | None: + """Resolves delta_timestamps by reading from the 'delta_indices' properties of the PreTrainedConfig. + + Args: + cfg (PreTrainedConfig): The PreTrainedConfig to read delta_indices from. + ds_meta (LeRobotDatasetMetadata): The dataset from which features and fps are used to build + delta_timestamps against. + + Returns: + dict[str, list] | None: A dictionary of delta_timestamps, e.g.: + { + "observation.state": [-0.04, -0.02, 0] + "observation.action": [-0.02, 0, 0.02] + } + returns `None` if the resulting dict is empty. + """ + delta_timestamps = {} + for key in ds_meta.features: + if key == "next.reward" and cfg.reward_delta_indices is not None: + delta_timestamps[key] = [i / ds_meta.fps for i in cfg.reward_delta_indices] + if key == "action" and cfg.action_delta_indices is not None: + delta_timestamps[key] = [i / ds_meta.fps for i in cfg.action_delta_indices] + if key.startswith("observation.") and cfg.observation_delta_indices is not None: + delta_timestamps[key] = [i / ds_meta.fps for i in cfg.observation_delta_indices] + + if len(delta_timestamps) == 0: + delta_timestamps = None + + return delta_timestamps + + +def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDataset: + """Handles the logic of setting up delta timestamps and image transforms before creating a dataset. + + Args: + cfg (TrainPipelineConfig): A TrainPipelineConfig config which contains a DatasetConfig and a PreTrainedConfig. + + Raises: + NotImplementedError: The MultiLeRobotDataset is currently deactivated. + + Returns: + LeRobotDataset | MultiLeRobotDataset + """ + image_transforms = ( + ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None + ) + + if isinstance(cfg.dataset.repo_id, str): + ds_meta = LeRobotDatasetMetadata( + cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision + ) + delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta) + dataset = LeRobotDataset( + cfg.dataset.repo_id, + root=cfg.dataset.root, + episodes=cfg.dataset.episodes, + delta_timestamps=delta_timestamps, + image_transforms=image_transforms, + revision=cfg.dataset.revision, + video_backend=cfg.dataset.video_backend, + ) + else: + raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.") + dataset = MultiLeRobotDataset( + cfg.dataset.repo_id, + # TODO(aliberts): add proper support for multi dataset + # delta_timestamps=delta_timestamps, + image_transforms=image_transforms, + video_backend=cfg.dataset.video_backend, + ) + logging.info( + "Multiple datasets were provided. Applied the following index mapping to the provided datasets: " + f"{pformat(dataset.repo_id_to_index, indent=2)}" + ) + + if cfg.dataset.use_imagenet_stats: + for key in dataset.meta.camera_keys: + for stats_type, stats in IMAGENET_STATS.items(): + dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32) + + return dataset diff --git a/lerobot/common/datasets/image_writer.py b/lerobot/common/datasets/image_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..af295f124aea3028a6be6a30bf71596b253d6f8e --- /dev/null +++ b/lerobot/common/datasets/image_writer.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import multiprocessing +import queue +import threading +from pathlib import Path + +import numpy as np +import PIL.Image +import torch + + +def safe_stop_image_writer(func): + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + dataset = kwargs.get("dataset") + image_writer = getattr(dataset, "image_writer", None) if dataset else None + if image_writer is not None: + print("Waiting for image writer to terminate...") + image_writer.stop() + raise e + + return wrapper + + +def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image: + # TODO(aliberts): handle 1 channel and 4 for depth images + if image_array.ndim != 3: + raise ValueError(f"The array has {image_array.ndim} dimensions, but 3 is expected for an image.") + + if image_array.shape[0] == 3: + # Transpose from pytorch convention (C, H, W) to (H, W, C) + image_array = image_array.transpose(1, 2, 0) + + elif image_array.shape[-1] != 3: + raise NotImplementedError( + f"The image has {image_array.shape[-1]} channels, but 3 is required for now." + ) + + if image_array.dtype != np.uint8: + if range_check: + max_ = image_array.max().item() + min_ = image_array.min().item() + if max_ > 1.0 or min_ < 0.0: + raise ValueError( + "The image data type is float, which requires values in the range [0.0, 1.0]. " + f"However, the provided range is [{min_}, {max_}]. Please adjust the range or " + "provide a uint8 image with values in the range [0, 255]." + ) + + image_array = (image_array * 255).astype(np.uint8) + + return PIL.Image.fromarray(image_array) + + +def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path): + try: + if isinstance(image, np.ndarray): + img = image_array_to_pil_image(image) + elif isinstance(image, PIL.Image.Image): + img = image + else: + raise TypeError(f"Unsupported image type: {type(image)}") + img.save(fpath) + except Exception as e: + print(f"Error writing image {fpath}: {e}") + + +def worker_thread_loop(queue: queue.Queue): + while True: + item = queue.get() + if item is None: + queue.task_done() + break + image_array, fpath = item + write_image(image_array, fpath) + queue.task_done() + + +def worker_process(queue: queue.Queue, num_threads: int): + threads = [] + for _ in range(num_threads): + t = threading.Thread(target=worker_thread_loop, args=(queue,)) + t.daemon = True + t.start() + threads.append(t) + for t in threads: + t.join() + + +class AsyncImageWriter: + """ + This class abstract away the initialisation of processes or/and threads to + save images on disk asynchronously, which is critical to control a robot and record data + at a high frame rate. + + When `num_processes=0`, it creates a threads pool of size `num_threads`. + When `num_processes>0`, it creates processes pool of size `num_processes`, where each subprocess starts + their own threads pool of size `num_threads`. + + The optimal number of processes and threads depends on your computer capabilities. + We advise to use 4 threads per camera with 0 processes. If the fps is not stable, try to increase or lower + the number of threads. If it is still not stable, try to use 1 subprocess, or more. + """ + + def __init__(self, num_processes: int = 0, num_threads: int = 1): + self.num_processes = num_processes + self.num_threads = num_threads + self.queue = None + self.threads = [] + self.processes = [] + self._stopped = False + + if num_threads <= 0 and num_processes <= 0: + raise ValueError("Number of threads and processes must be greater than zero.") + + if self.num_processes == 0: + # Use threading + self.queue = queue.Queue() + for _ in range(self.num_threads): + t = threading.Thread(target=worker_thread_loop, args=(self.queue,)) + t.daemon = True + t.start() + self.threads.append(t) + else: + # Use multiprocessing + self.queue = multiprocessing.JoinableQueue() + for _ in range(self.num_processes): + p = multiprocessing.Process(target=worker_process, args=(self.queue, self.num_threads)) + p.daemon = True + p.start() + self.processes.append(p) + + def save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path): + if isinstance(image, torch.Tensor): + # Convert tensor to numpy array to minimize main process time + image = image.cpu().numpy() + self.queue.put((image, fpath)) + + def wait_until_done(self): + self.queue.join() + + def stop(self): + if self._stopped: + return + + if self.num_processes == 0: + for _ in self.threads: + self.queue.put(None) + for t in self.threads: + t.join() + else: + num_nones = self.num_processes * self.num_threads + for _ in range(num_nones): + self.queue.put(None) + for p in self.processes: + p.join() + if p.is_alive(): + p.terminate() + self.queue.close() + self.queue.join_thread() + + self._stopped = True diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b8bb150b8628a9d815dcb5731c008f59afa4bb83 --- /dev/null +++ b/lerobot/common/datasets/lerobot_dataset.py @@ -0,0 +1,1190 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import contextlib +import logging +import shutil +from pathlib import Path +from typing import Callable + +import datasets +import numpy as np +import packaging.version +import PIL.Image +import torch +import torch.utils +from datasets import concatenate_datasets, load_dataset +from huggingface_hub import HfApi, snapshot_download +from huggingface_hub.constants import REPOCARD_NAME +from huggingface_hub.errors import RevisionNotFoundError + +from lerobot.common.constants import HF_LEROBOT_HOME +from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats +from lerobot.common.datasets.image_writer import AsyncImageWriter, write_image +from lerobot.common.datasets.utils import ( + DEFAULT_FEATURES, + DEFAULT_IMAGE_PATH, + INFO_PATH, + TASKS_PATH, + _validate_feature_names, + append_jsonlines, + backward_compatible_episodes_stats, + check_delta_timestamps, + check_timestamps_sync, + check_version_compatibility, + create_empty_dataset_info, + create_lerobot_dataset_card, + embed_images, + get_delta_indices, + get_episode_data_index, + get_hf_features_from_features, + get_safe_version, + hf_transform_to_torch, + is_valid_version, + load_episodes, + load_episodes_stats, + load_info, + load_stats, + load_tasks, + validate_episode_buffer, + validate_frame, + write_episode, + write_episode_stats, + write_info, + write_json, +) +from lerobot.common.datasets.video_utils import ( + VideoFrame, + decode_video_frames, + encode_video_frames, + get_safe_default_codec, + get_video_info, +) + +CODEBASE_VERSION = "v2.1" + + +class LeRobotDatasetMetadata: + def __init__( + self, + repo_id: str, + root: str | Path | None = None, + revision: str | None = None, + force_cache_sync: bool = False, + ): + self.repo_id = repo_id + self.revision = revision if revision else CODEBASE_VERSION + self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id + + try: + if force_cache_sync: + raise FileNotFoundError + self.load_metadata() + except (FileNotFoundError, NotADirectoryError): + if is_valid_version(self.revision): + self.revision = get_safe_version(self.repo_id, self.revision) + + (self.root / "meta").mkdir(exist_ok=True, parents=True) + self.pull_from_repo(allow_patterns="meta/") + self.load_metadata() + + def load_metadata(self): + self.info = load_info(self.root) + check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION) + self.tasks, self.task_to_task_index = load_tasks(self.root) + self.episodes = load_episodes(self.root) + if self._version < packaging.version.parse("v2.1"): + self.stats = load_stats(self.root) + self.episodes_stats = backward_compatible_episodes_stats(self.stats, self.episodes) + else: + self.episodes_stats = load_episodes_stats(self.root) + self.stats = aggregate_stats(list(self.episodes_stats.values())) + + def pull_from_repo( + self, + allow_patterns: list[str] | str | None = None, + ignore_patterns: list[str] | str | None = None, + ) -> None: + snapshot_download( + self.repo_id, + repo_type="dataset", + revision=self.revision, + local_dir=self.root, + allow_patterns=allow_patterns, + ignore_patterns=ignore_patterns, + ) + + @property + def _version(self) -> packaging.version.Version: + """Codebase version used to create this dataset.""" + return packaging.version.parse(self.info["codebase_version"]) + + def get_data_file_path(self, ep_index: int) -> Path: + ep_chunk = self.get_episode_chunk(ep_index) + fpath = self.data_path.format(episode_chunk=ep_chunk, episode_index=ep_index) + return Path(fpath) + + def get_video_file_path(self, ep_index: int, vid_key: str) -> Path: + ep_chunk = self.get_episode_chunk(ep_index) + fpath = self.video_path.format(episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_index) + return Path(fpath) + + def get_episode_chunk(self, ep_index: int) -> int: + return ep_index // self.chunks_size + + @property + def data_path(self) -> str: + """Formattable string for the parquet files.""" + return self.info["data_path"] + + @property + def video_path(self) -> str | None: + """Formattable string for the video files.""" + return self.info["video_path"] + + @property + def robot_type(self) -> str | None: + """Robot type used in recording this dataset.""" + return self.info["robot_type"] + + @property + def fps(self) -> int: + """Frames per second used during data collection.""" + return self.info["fps"] + + @property + def features(self) -> dict[str, dict]: + """All features contained in the dataset.""" + return self.info["features"] + + @property + def image_keys(self) -> list[str]: + """Keys to access visual modalities stored as images.""" + return [key for key, ft in self.features.items() if ft["dtype"] == "image"] + + @property + def video_keys(self) -> list[str]: + """Keys to access visual modalities stored as videos.""" + return [key for key, ft in self.features.items() if ft["dtype"] == "video"] + + @property + def camera_keys(self) -> list[str]: + """Keys to access visual modalities (regardless of their storage method).""" + return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]] + + @property + def names(self) -> dict[str, list | dict]: + """Names of the various dimensions of vector modalities.""" + return {key: ft["names"] for key, ft in self.features.items()} + + @property + def shapes(self) -> dict: + """Shapes for the different features.""" + return {key: tuple(ft["shape"]) for key, ft in self.features.items()} + + @property + def total_episodes(self) -> int: + """Total number of episodes available.""" + return self.info["total_episodes"] + + @property + def total_frames(self) -> int: + """Total number of frames saved in this dataset.""" + return self.info["total_frames"] + + @property + def total_tasks(self) -> int: + """Total number of different tasks performed in this dataset.""" + return self.info["total_tasks"] + + @property + def total_chunks(self) -> int: + """Total number of chunks (groups of episodes).""" + return self.info["total_chunks"] + + @property + def chunks_size(self) -> int: + """Max number of episodes per chunk.""" + return self.info["chunks_size"] + + def get_task_index(self, task: str) -> int | None: + """ + Given a task in natural language, returns its task_index if the task already exists in the dataset, + otherwise return None. + """ + return self.task_to_task_index.get(task, None) + + def add_task(self, task: str): + """ + Given a task in natural language, add it to the dictionary of tasks. + """ + if task in self.task_to_task_index: + raise ValueError(f"The task '{task}' already exists and can't be added twice.") + + task_index = self.info["total_tasks"] + self.task_to_task_index[task] = task_index + self.tasks[task_index] = task + self.info["total_tasks"] += 1 + + task_dict = { + "task_index": task_index, + "task": task, + } + append_jsonlines(task_dict, self.root / TASKS_PATH) + + def save_episode( + self, + episode_index: int, + episode_length: int, + episode_tasks: list[str], + episode_stats: dict[str, dict], + ) -> None: + self.info["total_episodes"] += 1 + self.info["total_frames"] += episode_length + + chunk = self.get_episode_chunk(episode_index) + if chunk >= self.total_chunks: + self.info["total_chunks"] += 1 + + self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"} + self.info["total_videos"] += len(self.video_keys) + if len(self.video_keys) > 0: + self.update_video_info() + + write_info(self.info, self.root) + + episode_dict = { + "episode_index": episode_index, + "tasks": episode_tasks, + "length": episode_length, + } + self.episodes[episode_index] = episode_dict + write_episode(episode_dict, self.root) + + self.episodes_stats[episode_index] = episode_stats + self.stats = aggregate_stats([self.stats, episode_stats]) if self.stats else episode_stats + write_episode_stats(episode_index, episode_stats, self.root) + + def update_video_info(self) -> None: + """ + Warning: this function writes info from first episode videos, implicitly assuming that all videos have + been encoded the same way. Also, this means it assumes the first episode exists. + """ + for key in self.video_keys: + if not self.features[key].get("info", None): + video_path = self.root / self.get_video_file_path(ep_index=0, vid_key=key) + self.info["features"][key]["info"] = get_video_info(video_path) + + def __repr__(self): + feature_keys = list(self.features) + return ( + f"{self.__class__.__name__}({{\n" + f" Repository ID: '{self.repo_id}',\n" + f" Total episodes: '{self.total_episodes}',\n" + f" Total frames: '{self.total_frames}',\n" + f" Features: '{feature_keys}',\n" + "})',\n" + ) + + @classmethod + def create( + cls, + repo_id: str, + fps: int, + features: dict, + robot_type: str | None = None, + root: str | Path | None = None, + use_videos: bool = True, + ) -> "LeRobotDatasetMetadata": + """Creates metadata for a LeRobotDataset.""" + obj = cls.__new__(cls) + obj.repo_id = repo_id + obj.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id + + obj.root.mkdir(parents=True, exist_ok=False) + + # TODO(aliberts, rcadene): implement sanity check for features + features = {**features, **DEFAULT_FEATURES} + _validate_feature_names(features) + + obj.tasks, obj.task_to_task_index = {}, {} + obj.episodes_stats, obj.stats, obj.episodes = {}, {}, {} + obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, features, use_videos, robot_type) + if len(obj.video_keys) > 0 and not use_videos: + raise ValueError() + write_json(obj.info, obj.root / INFO_PATH) + obj.revision = None + return obj + + +class LeRobotDataset(torch.utils.data.Dataset): + def __init__( + self, + repo_id: str, + root: str | Path | None = None, + episodes: list[int] | None = None, + image_transforms: Callable | None = None, + delta_timestamps: dict[list[float]] | None = None, + tolerance_s: float = 1e-4, + revision: str | None = None, + force_cache_sync: bool = False, + download_videos: bool = True, + video_backend: str | None = None, + ): + """ + 2 modes are available for instantiating this class, depending on 2 different use cases: + + 1. Your dataset already exists: + - On your local disk in the 'root' folder. This is typically the case when you recorded your + dataset locally and you may or may not have pushed it to the hub yet. Instantiating this class + with 'root' will load your dataset directly from disk. This can happen while you're offline (no + internet connection). + + - On the Hugging Face Hub at the address https://huggingface.co/datasets/{repo_id} and not on + your local disk in the 'root' folder. Instantiating this class with this 'repo_id' will download + the dataset from that address and load it, pending your dataset is compliant with + codebase_version v2.0. If your dataset has been created before this new format, you will be + prompted to convert it using our conversion script from v1.6 to v2.0, which you can find at + lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py. + + + 2. Your dataset doesn't already exists (either on local disk or on the Hub): you can create an empty + LeRobotDataset with the 'create' classmethod. This can be used for recording a dataset or port an + existing dataset to the LeRobotDataset format. + + + In terms of files, LeRobotDataset encapsulates 3 main things: + - metadata: + - info contains various information about the dataset like shapes, keys, fps etc. + - stats stores the dataset statistics of the different modalities for normalization + - tasks contains the prompts for each task of the dataset, which can be used for + task-conditioned training. + - hf_dataset (from datasets.Dataset), which will read any values from parquet files. + - videos (optional) from which frames are loaded to be synchronous with data from parquet files. + + A typical LeRobotDataset looks like this from its root path: + . + ├── data + │ ├── chunk-000 + │ │ ├── episode_000000.parquet + │ │ ├── episode_000001.parquet + │ │ ├── episode_000002.parquet + │ │ └── ... + │ ├── chunk-001 + │ │ ├── episode_001000.parquet + │ │ ├── episode_001001.parquet + │ │ ├── episode_001002.parquet + │ │ └── ... + │ └── ... + ├── meta + │ ├── episodes.jsonl + │ ├── info.json + │ ├── stats.json + │ └── tasks.jsonl + └── videos + ├── chunk-000 + │ ├── observation.images.laptop + │ │ ├── episode_000000.mp4 + │ │ ├── episode_000001.mp4 + │ │ ├── episode_000002.mp4 + │ │ └── ... + │ ├── observation.images.phone + │ │ ├── episode_000000.mp4 + │ │ ├── episode_000001.mp4 + │ │ ├── episode_000002.mp4 + │ │ └── ... + ├── chunk-001 + └── ... + + Note that this file-based structure is designed to be as versatile as possible. The files are split by + episodes which allows a more granular control over which episodes one wants to use and download. The + structure of the dataset is entirely described in the info.json file, which can be easily downloaded + or viewed directly on the hub before downloading any actual data. The type of files used are very + simple and do not need complex tools to be read, it only uses .parquet, .json and .mp4 files (and .md + for the README). + + Args: + repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset + will be stored under root/repo_id. + root (Path | None, optional): Local directory to use for downloading/writing files. You can also + set the LEROBOT_HOME environment variable to point to a different location. Defaults to + '~/.cache/huggingface/lerobot'. + episodes (list[int] | None, optional): If specified, this will only load episodes specified by + their episode_index in this list. Defaults to None. + image_transforms (Callable | None, optional): You can pass standard v2 image transforms from + torchvision.transforms.v2 here which will be applied to visual modalities (whether they come + from videos or images). Defaults to None. + delta_timestamps (dict[list[float]] | None, optional): _description_. Defaults to None. + tolerance_s (float, optional): Tolerance in seconds used to ensure data timestamps are actually in + sync with the fps value. It is used at the init of the dataset to make sure that each + timestamps is separated to the next by 1/fps +/- tolerance_s. This also applies to frames + decoded from video files. It is also used to check that `delta_timestamps` (when provided) are + multiples of 1/fps. Defaults to 1e-4. + revision (str, optional): An optional Git revision id which can be a branch name, a tag, or a + commit hash. Defaults to current codebase version tag. + sync_cache_first (bool, optional): Flag to sync and refresh local files first. If True and files + are already present in the local cache, this will be faster. However, files loaded might not + be in sync with the version on the hub, especially if you specified 'revision'. Defaults to + False. + download_videos (bool, optional): Flag to download the videos. Note that when set to True but the + video files are already present on local disk, they won't be downloaded again. Defaults to + True. + video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'. + You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision. + """ + super().__init__() + self.repo_id = repo_id + self.root = Path(root) if root else HF_LEROBOT_HOME / repo_id + self.image_transforms = image_transforms + self.delta_timestamps = delta_timestamps + self.episodes = episodes + self.tolerance_s = tolerance_s + self.revision = revision if revision else CODEBASE_VERSION + self.video_backend = video_backend if video_backend else get_safe_default_codec() + self.delta_indices = None + + # Unused attributes + self.image_writer = None + self.episode_buffer = None + + self.root.mkdir(exist_ok=True, parents=True) + + # Load metadata + self.meta = LeRobotDatasetMetadata( + self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync + ) + if self.episodes is not None and self.meta._version >= packaging.version.parse("v2.1"): + episodes_stats = [self.meta.episodes_stats[ep_idx] for ep_idx in self.episodes] + self.stats = aggregate_stats(episodes_stats) + + # Load actual data + try: + if force_cache_sync: + raise FileNotFoundError + assert all((self.root / fpath).is_file() for fpath in self.get_episodes_file_paths()) + self.hf_dataset = self.load_hf_dataset() + except (AssertionError, FileNotFoundError, NotADirectoryError): + self.revision = get_safe_version(self.repo_id, self.revision) + self.download_episodes(download_videos) + self.hf_dataset = self.load_hf_dataset() + + self.episode_data_index = get_episode_data_index(self.meta.episodes, self.episodes) + + # Check timestamps + timestamps = torch.stack(self.hf_dataset["timestamp"]).numpy() + episode_indices = torch.stack(self.hf_dataset["episode_index"]).numpy() + ep_data_index_np = {k: t.numpy() for k, t in self.episode_data_index.items()} + check_timestamps_sync(timestamps, episode_indices, ep_data_index_np, self.fps, self.tolerance_s) + + # Setup delta_indices + if self.delta_timestamps is not None: + check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s) + self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps) + + def push_to_hub( + self, + branch: str | None = None, + tags: list | None = None, + license: str | None = "apache-2.0", + tag_version: bool = True, + push_videos: bool = True, + private: bool = False, + allow_patterns: list[str] | str | None = None, + upload_large_folder: bool = False, + **card_kwargs, + ) -> None: + ignore_patterns = ["images/"] + if not push_videos: + ignore_patterns.append("videos/") + + hub_api = HfApi() + hub_api.create_repo( + repo_id=self.repo_id, + private=private, + repo_type="dataset", + exist_ok=True, + ) + if branch: + hub_api.create_branch( + repo_id=self.repo_id, + branch=branch, + revision=self.revision, + repo_type="dataset", + exist_ok=True, + ) + + upload_kwargs = { + "repo_id": self.repo_id, + "folder_path": self.root, + "repo_type": "dataset", + "revision": branch, + "allow_patterns": allow_patterns, + "ignore_patterns": ignore_patterns, + } + if upload_large_folder: + hub_api.upload_large_folder(**upload_kwargs) + else: + hub_api.upload_folder(**upload_kwargs) + + if not hub_api.file_exists(self.repo_id, REPOCARD_NAME, repo_type="dataset", revision=branch): + card = create_lerobot_dataset_card( + tags=tags, dataset_info=self.meta.info, license=license, **card_kwargs + ) + card.push_to_hub(repo_id=self.repo_id, repo_type="dataset", revision=branch) + + if tag_version: + with contextlib.suppress(RevisionNotFoundError): + hub_api.delete_tag(self.repo_id, tag=CODEBASE_VERSION, repo_type="dataset") + hub_api.create_tag(self.repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset") + + def pull_from_repo( + self, + allow_patterns: list[str] | str | None = None, + ignore_patterns: list[str] | str | None = None, + ) -> None: + snapshot_download( + self.repo_id, + repo_type="dataset", + revision=self.revision, + local_dir=self.root, + allow_patterns=allow_patterns, + ignore_patterns=ignore_patterns, + ) + + def download_episodes(self, download_videos: bool = True) -> None: + """Downloads the dataset from the given 'repo_id' at the provided version. If 'episodes' is given, this + will only download those episodes (selected by their episode_index). If 'episodes' is None, the whole + dataset will be downloaded. Thanks to the behavior of snapshot_download, if the files are already present + in 'local_dir', they won't be downloaded again. + """ + # TODO(rcadene, aliberts): implement faster transfer + # https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads + files = None + ignore_patterns = None if download_videos else "videos/" + if self.episodes is not None: + files = self.get_episodes_file_paths() + + self.pull_from_repo(allow_patterns=files, ignore_patterns=ignore_patterns) + + def get_episodes_file_paths(self) -> list[Path]: + episodes = self.episodes if self.episodes is not None else list(range(self.meta.total_episodes)) + fpaths = [str(self.meta.get_data_file_path(ep_idx)) for ep_idx in episodes] + if len(self.meta.video_keys) > 0: + video_files = [ + str(self.meta.get_video_file_path(ep_idx, vid_key)) + for vid_key in self.meta.video_keys + for ep_idx in episodes + ] + fpaths += video_files + + return fpaths + + def load_hf_dataset(self) -> datasets.Dataset: + """hf_dataset contains all the observations, states, actions, rewards, etc.""" + if self.episodes is None: + path = str(self.root / "data") + hf_dataset = load_dataset("parquet", data_dir=path, split="train") + else: + files = [str(self.root / self.meta.get_data_file_path(ep_idx)) for ep_idx in self.episodes] + hf_dataset = load_dataset("parquet", data_files=files, split="train") + + # TODO(aliberts): hf_dataset.set_format("torch") + hf_dataset.set_transform(hf_transform_to_torch) + return hf_dataset + + def create_hf_dataset(self) -> datasets.Dataset: + features = get_hf_features_from_features(self.features) + ft_dict = {col: [] for col in features} + hf_dataset = datasets.Dataset.from_dict(ft_dict, features=features, split="train") + + # TODO(aliberts): hf_dataset.set_format("torch") + hf_dataset.set_transform(hf_transform_to_torch) + return hf_dataset + + @property + def fps(self) -> int: + """Frames per second used during data collection.""" + return self.meta.fps + + @property + def num_frames(self) -> int: + """Number of frames in selected episodes.""" + return len(self.hf_dataset) if self.hf_dataset is not None else self.meta.total_frames + + @property + def num_episodes(self) -> int: + """Number of episodes selected.""" + return len(self.episodes) if self.episodes is not None else self.meta.total_episodes + + @property + def features(self) -> dict[str, dict]: + return self.meta.features + + @property + def hf_features(self) -> datasets.Features: + """Features of the hf_dataset.""" + if self.hf_dataset is not None: + return self.hf_dataset.features + else: + return get_hf_features_from_features(self.features) + + def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]: + ep_start = self.episode_data_index["from"][ep_idx] + ep_end = self.episode_data_index["to"][ep_idx] + query_indices = { + key: [max(ep_start.item(), min(ep_end.item() - 1, idx + delta)) for delta in delta_idx] + for key, delta_idx in self.delta_indices.items() + } + padding = { # Pad values outside of current episode range + f"{key}_is_pad": torch.BoolTensor( + [(idx + delta < ep_start.item()) | (idx + delta >= ep_end.item()) for delta in delta_idx] + ) + for key, delta_idx in self.delta_indices.items() + } + return query_indices, padding + + def _get_query_timestamps( + self, + current_ts: float, + query_indices: dict[str, list[int]] | None = None, + ) -> dict[str, list[float]]: + query_timestamps = {} + for key in self.meta.video_keys: + if query_indices is not None and key in query_indices: + timestamps = self.hf_dataset.select(query_indices[key])["timestamp"] + query_timestamps[key] = torch.stack(timestamps).tolist() + else: + query_timestamps[key] = [current_ts] + + return query_timestamps + + def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict: + return { + key: torch.stack(self.hf_dataset.select(q_idx)[key]) + for key, q_idx in query_indices.items() + if key not in self.meta.video_keys + } + + def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]: + """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function + in the main process (e.g. by using a second Dataloader with num_workers=0). It will result in a + Segmentation Fault. This probably happens because a memory reference to the video loader is created in + the main process and a subprocess fails to access it. + """ + item = {} + for vid_key, query_ts in query_timestamps.items(): + video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key) + frames = decode_video_frames(video_path, query_ts, self.tolerance_s, self.video_backend) + item[vid_key] = frames.squeeze(0) + + return item + + def _add_padding_keys(self, item: dict, padding: dict[str, list[bool]]) -> dict: + for key, val in padding.items(): + item[key] = torch.BoolTensor(val) + return item + + def __len__(self): + return self.num_frames + + def __getitem__(self, idx) -> dict: + item = self.hf_dataset[idx] + ep_idx = item["episode_index"].item() + + query_indices = None + if self.delta_indices is not None: + query_indices, padding = self._get_query_indices(idx, ep_idx) + query_result = self._query_hf_dataset(query_indices) + item = {**item, **padding} + for key, val in query_result.items(): + item[key] = val + + if len(self.meta.video_keys) > 0: + current_ts = item["timestamp"].item() + query_timestamps = self._get_query_timestamps(current_ts, query_indices) + video_frames = self._query_videos(query_timestamps, ep_idx) + item = {**video_frames, **item} + + if self.image_transforms is not None: + image_keys = self.meta.camera_keys + for cam in image_keys: + item[cam] = self.image_transforms(item[cam]) + + # Add task as a string + task_idx = item["task_index"].item() + item["task"] = self.meta.tasks[task_idx] + + return item + + def __repr__(self): + feature_keys = list(self.features) + return ( + f"{self.__class__.__name__}({{\n" + f" Repository ID: '{self.repo_id}',\n" + f" Number of selected episodes: '{self.num_episodes}',\n" + f" Number of selected samples: '{self.num_frames}',\n" + f" Features: '{feature_keys}',\n" + "})',\n" + ) + + def create_episode_buffer(self, episode_index: int | None = None) -> dict: + current_ep_idx = self.meta.total_episodes if episode_index is None else episode_index + ep_buffer = {} + # size and task are special cases that are not in self.features + ep_buffer["size"] = 0 + ep_buffer["task"] = [] + for key in self.features: + ep_buffer[key] = current_ep_idx if key == "episode_index" else [] + return ep_buffer + + def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path: + fpath = DEFAULT_IMAGE_PATH.format( + image_key=image_key, episode_index=episode_index, frame_index=frame_index + ) + return self.root / fpath + + def _save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path) -> None: + if self.image_writer is None: + if isinstance(image, torch.Tensor): + image = image.cpu().numpy() + write_image(image, fpath) + else: + self.image_writer.save_image(image=image, fpath=fpath) + + def add_frame(self, frame: dict, task: str, timestamp: float | None = None) -> None: + """ + This function only adds the frame to the episode_buffer. Apart from images — which are written in a + temporary directory — nothing is written to disk. To save those frames, the 'save_episode()' method + then needs to be called. + """ + # Convert torch to numpy if needed + for name in frame: + if isinstance(frame[name], torch.Tensor): + frame[name] = frame[name].numpy() + + validate_frame(frame, self.features) + + if self.episode_buffer is None: + self.episode_buffer = self.create_episode_buffer() + + # Automatically add frame_index and timestamp to episode buffer + frame_index = self.episode_buffer["size"] + if timestamp is None: + timestamp = frame_index / self.fps + self.episode_buffer["frame_index"].append(frame_index) + self.episode_buffer["timestamp"].append(timestamp) + self.episode_buffer["task"].append(task) + + # Add frame features to episode_buffer + for key in frame: + if key not in self.features: + raise ValueError( + f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'." + ) + + if self.features[key]["dtype"] in ["image", "video"]: + img_path = self._get_image_file_path( + episode_index=self.episode_buffer["episode_index"], image_key=key, frame_index=frame_index + ) + if frame_index == 0: + img_path.parent.mkdir(parents=True, exist_ok=True) + self._save_image(frame[key], img_path) + self.episode_buffer[key].append(str(img_path)) + else: + self.episode_buffer[key].append(frame[key]) + + self.episode_buffer["size"] += 1 + + def save_episode(self, episode_data: dict | None = None) -> None: + """ + This will save to disk the current episode in self.episode_buffer. + + Args: + episode_data (dict | None, optional): Dict containing the episode data to save. If None, this will + save the current episode in self.episode_buffer, which is filled with 'add_frame'. Defaults to + None. + """ + if not episode_data: + episode_buffer = self.episode_buffer + + validate_episode_buffer(episode_buffer, self.meta.total_episodes, self.features) + + # size and task are special cases that won't be added to hf_dataset + episode_length = episode_buffer.pop("size") + tasks = episode_buffer.pop("task") + episode_tasks = list(set(tasks)) + episode_index = episode_buffer["episode_index"] + + episode_buffer["index"] = np.arange(self.meta.total_frames, self.meta.total_frames + episode_length) + episode_buffer["episode_index"] = np.full((episode_length,), episode_index) + + # Add new tasks to the tasks dictionary + for task in episode_tasks: + task_index = self.meta.get_task_index(task) + if task_index is None: + self.meta.add_task(task) + + # Given tasks in natural language, find their corresponding task indices + episode_buffer["task_index"] = np.array([self.meta.get_task_index(task) for task in tasks]) + + for key, ft in self.features.items(): + # index, episode_index, task_index are already processed above, and image and video + # are processed separately by storing image path and frame info as meta data + if key in ["index", "episode_index", "task_index"] or ft["dtype"] in ["image", "video"]: + continue + episode_buffer[key] = np.stack(episode_buffer[key]) + + self._wait_image_writer() + self._save_episode_table(episode_buffer, episode_index) + ep_stats = compute_episode_stats(episode_buffer, self.features) + + if len(self.meta.video_keys) > 0: + video_paths = self.encode_episode_videos(episode_index) + for key in self.meta.video_keys: + episode_buffer[key] = video_paths[key] + + # `meta.save_episode` be executed after encoding the videos + self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats) + + ep_data_index = get_episode_data_index(self.meta.episodes, [episode_index]) + ep_data_index_np = {k: t.numpy() for k, t in ep_data_index.items()} + check_timestamps_sync( + episode_buffer["timestamp"], + episode_buffer["episode_index"], + ep_data_index_np, + self.fps, + self.tolerance_s, + ) + + video_files = list(self.root.rglob("*.mp4")) + assert len(video_files) == self.num_episodes * len(self.meta.video_keys) + + parquet_files = list(self.root.rglob("*.parquet")) + assert len(parquet_files) == self.num_episodes + + # delete images + img_dir = self.root / "images" + if img_dir.is_dir(): + shutil.rmtree(self.root / "images") + + if not episode_data: # Reset the buffer + self.episode_buffer = self.create_episode_buffer() + + def _save_episode_table(self, episode_buffer: dict, episode_index: int) -> None: + episode_dict = {key: episode_buffer[key] for key in self.hf_features} + ep_dataset = datasets.Dataset.from_dict(episode_dict, features=self.hf_features, split="train") + ep_dataset = embed_images(ep_dataset) + self.hf_dataset = concatenate_datasets([self.hf_dataset, ep_dataset]) + self.hf_dataset.set_transform(hf_transform_to_torch) + ep_data_path = self.root / self.meta.get_data_file_path(ep_index=episode_index) + ep_data_path.parent.mkdir(parents=True, exist_ok=True) + ep_dataset.to_parquet(ep_data_path) + + def clear_episode_buffer(self) -> None: + episode_index = self.episode_buffer["episode_index"] + if self.image_writer is not None: + for cam_key in self.meta.camera_keys: + img_dir = self._get_image_file_path( + episode_index=episode_index, image_key=cam_key, frame_index=0 + ).parent + if img_dir.is_dir(): + shutil.rmtree(img_dir) + + # Reset the buffer + self.episode_buffer = self.create_episode_buffer() + + def start_image_writer(self, num_processes: int = 0, num_threads: int = 4) -> None: + if isinstance(self.image_writer, AsyncImageWriter): + logging.warning( + "You are starting a new AsyncImageWriter that is replacing an already existing one in the dataset." + ) + + self.image_writer = AsyncImageWriter( + num_processes=num_processes, + num_threads=num_threads, + ) + + def stop_image_writer(self) -> None: + """ + Whenever wrapping this dataset inside a parallelized DataLoader, this needs to be called first to + remove the image_writer in order for the LeRobotDataset object to be picklable and parallelized. + """ + if self.image_writer is not None: + self.image_writer.stop() + self.image_writer = None + + def _wait_image_writer(self) -> None: + """Wait for asynchronous image writer to finish.""" + if self.image_writer is not None: + self.image_writer.wait_until_done() + + def encode_videos(self) -> None: + """ + Use ffmpeg to convert frames stored as png into mp4 videos. + Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding, + since video encoding with ffmpeg is already using multithreading. + """ + for ep_idx in range(self.meta.total_episodes): + self.encode_episode_videos(ep_idx) + + def encode_episode_videos(self, episode_index: int) -> dict: + """ + Use ffmpeg to convert frames stored as png into mp4 videos. + Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding, + since video encoding with ffmpeg is already using multithreading. + """ + video_paths = {} + for key in self.meta.video_keys: + video_path = self.root / self.meta.get_video_file_path(episode_index, key) + video_paths[key] = str(video_path) + if video_path.is_file(): + # Skip if video is already encoded. Could be the case when resuming data recording. + continue + img_dir = self._get_image_file_path( + episode_index=episode_index, image_key=key, frame_index=0 + ).parent + encode_video_frames(img_dir, video_path, self.fps, overwrite=True) + + return video_paths + + @classmethod + def create( + cls, + repo_id: str, + fps: int, + features: dict, + root: str | Path | None = None, + robot_type: str | None = None, + use_videos: bool = True, + tolerance_s: float = 1e-4, + image_writer_processes: int = 0, + image_writer_threads: int = 0, + video_backend: str | None = None, + ) -> "LeRobotDataset": + """Create a LeRobot Dataset from scratch in order to record data.""" + obj = cls.__new__(cls) + obj.meta = LeRobotDatasetMetadata.create( + repo_id=repo_id, + fps=fps, + robot_type=robot_type, + features=features, + root=root, + use_videos=use_videos, + ) + obj.repo_id = obj.meta.repo_id + obj.root = obj.meta.root + obj.revision = None + obj.tolerance_s = tolerance_s + obj.image_writer = None + + if image_writer_processes or image_writer_threads: + obj.start_image_writer(image_writer_processes, image_writer_threads) + + # TODO(aliberts, rcadene, alexander-soare): Merge this with OnlineBuffer/DataBuffer + obj.episode_buffer = obj.create_episode_buffer() + + obj.episodes = None + obj.hf_dataset = obj.create_hf_dataset() + obj.image_transforms = None + obj.delta_timestamps = None + obj.delta_indices = None + obj.episode_data_index = None + obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec() + return obj + + +class MultiLeRobotDataset(torch.utils.data.Dataset): + """A dataset consisting of multiple underlying `LeRobotDataset`s. + + The underlying `LeRobotDataset`s are effectively concatenated, and this class adopts much of the API + structure of `LeRobotDataset`. + """ + + def __init__( + self, + repo_ids: list[str], + root: str | Path | None = None, + episodes: dict | None = None, + image_transforms: Callable | None = None, + delta_timestamps: dict[list[float]] | None = None, + tolerances_s: dict | None = None, + download_videos: bool = True, + video_backend: str | None = None, + ): + super().__init__() + self.repo_ids = repo_ids + self.root = Path(root) if root else HF_LEROBOT_HOME + self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001) + # Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which + # are handled by this class. + self._datasets = [ + LeRobotDataset( + repo_id, + root=self.root / repo_id, + episodes=episodes[repo_id] if episodes else None, + image_transforms=image_transforms, + delta_timestamps=delta_timestamps, + tolerance_s=self.tolerances_s[repo_id], + download_videos=download_videos, + video_backend=video_backend, + ) + for repo_id in repo_ids + ] + + # Disable any data keys that are not common across all of the datasets. Note: we may relax this + # restriction in future iterations of this class. For now, this is necessary at least for being able + # to use PyTorch's default DataLoader collate function. + self.disabled_features = set() + intersection_features = set(self._datasets[0].features) + for ds in self._datasets: + intersection_features.intersection_update(ds.features) + if len(intersection_features) == 0: + raise RuntimeError( + "Multiple datasets were provided but they had no keys common to all of them. " + "The multi-dataset functionality currently only keeps common keys." + ) + for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True): + extra_keys = set(ds.features).difference(intersection_features) + logging.warning( + f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the " + "other datasets." + ) + self.disabled_features.update(extra_keys) + + self.image_transforms = image_transforms + self.delta_timestamps = delta_timestamps + # TODO(rcadene, aliberts): We should not perform this aggregation for datasets + # with multiple robots of different ranges. Instead we should have one normalization + # per robot. + self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets]) + + @property + def repo_id_to_index(self): + """Return a mapping from dataset repo_id to a dataset index automatically created by this class. + + This index is incorporated as a data key in the dictionary returned by `__getitem__`. + """ + return {repo_id: i for i, repo_id in enumerate(self.repo_ids)} + + @property + def repo_index_to_id(self): + """Return the inverse mapping if repo_id_to_index.""" + return {v: k for k, v in self.repo_id_to_index} + + @property + def fps(self) -> int: + """Frames per second used during data collection. + + NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info. + """ + return self._datasets[0].meta.info["fps"] + + @property + def video(self) -> bool: + """Returns True if this dataset loads video frames from mp4 files. + + Returns False if it only loads images from png files. + + NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info. + """ + return self._datasets[0].meta.info.get("video", False) + + @property + def features(self) -> datasets.Features: + features = {} + for dataset in self._datasets: + features.update({k: v for k, v in dataset.hf_features.items() if k not in self.disabled_features}) + return features + + @property + def camera_keys(self) -> list[str]: + """Keys to access image and video stream from cameras.""" + keys = [] + for key, feats in self.features.items(): + if isinstance(feats, (datasets.Image, VideoFrame)): + keys.append(key) + return keys + + @property + def video_frame_keys(self) -> list[str]: + """Keys to access video frames that requires to be decoded into images. + + Note: It is empty if the dataset contains images only, + or equal to `self.cameras` if the dataset contains videos only, + or can even be a subset of `self.cameras` in a case of a mixed image/video dataset. + """ + video_frame_keys = [] + for key, feats in self.features.items(): + if isinstance(feats, VideoFrame): + video_frame_keys.append(key) + return video_frame_keys + + @property + def num_frames(self) -> int: + """Number of samples/frames.""" + return sum(d.num_frames for d in self._datasets) + + @property + def num_episodes(self) -> int: + """Number of episodes.""" + return sum(d.num_episodes for d in self._datasets) + + @property + def tolerance_s(self) -> float: + """Tolerance in seconds used to discard loaded frames when their timestamps + are not close enough from the requested frames. It is only used when `delta_timestamps` + is provided or when loading video frames from mp4 files. + """ + # 1e-4 to account for possible numerical error + return 1 / self.fps - 1e-4 + + def __len__(self): + return self.num_frames + + def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: + if idx >= len(self): + raise IndexError(f"Index {idx} out of bounds.") + # Determine which dataset to get an item from based on the index. + start_idx = 0 + dataset_idx = 0 + for dataset in self._datasets: + if idx >= start_idx + dataset.num_frames: + start_idx += dataset.num_frames + dataset_idx += 1 + continue + break + else: + raise AssertionError("We expect the loop to break out as long as the index is within bounds.") + item = self._datasets[dataset_idx][idx - start_idx] + item["dataset_index"] = torch.tensor(dataset_idx) + for data_key in self.disabled_features: + if data_key in item: + del item[data_key] + + return item + + def __repr__(self): + return ( + f"{self.__class__.__name__}(\n" + f" Repository IDs: '{self.repo_ids}',\n" + f" Number of Samples: {self.num_frames},\n" + f" Number of Episodes: {self.num_episodes},\n" + f" Type: {'video (.mp4)' if self.video else 'image (.png)'},\n" + f" Recorded Frames per Second: {self.fps},\n" + f" Camera Keys: {self.camera_keys},\n" + f" Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n" + f" Transformations: {self.image_transforms},\n" + f")" + ) diff --git a/lerobot/common/datasets/online_buffer.py b/lerobot/common/datasets/online_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..246da4a673deee223a7c4bf687daeef448760f65 --- /dev/null +++ b/lerobot/common/datasets/online_buffer.py @@ -0,0 +1,384 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""An online buffer for the online training loop in train.py + +Note to maintainers: This duplicates some logic from LeRobotDataset and EpisodeAwareSampler. We should +consider converging to one approach. Here we have opted to use numpy.memmap to back the data buffer. It's much +faster than using HuggingFace Datasets as there's no conversion to an intermediate non-python object. Also it +supports in-place slicing and mutation which is very handy for a dynamic buffer. +""" + +import os +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + + +def _make_memmap_safe(**kwargs) -> np.memmap: + """Make a numpy memmap with checks on available disk space first. + + Expected kwargs are: "filename", "dtype" (must by np.dtype), "mode" and "shape" + + For information on dtypes: + https://numpy.org/doc/stable/reference/arrays.dtypes.html#arrays-dtypes-constructing + """ + if kwargs["mode"].startswith("w"): + required_space = kwargs["dtype"].itemsize * np.prod(kwargs["shape"]) # bytes + stats = os.statvfs(Path(kwargs["filename"]).parent) + available_space = stats.f_bavail * stats.f_frsize # bytes + if required_space >= available_space * 0.8: + raise RuntimeError( + f"You're about to take up {required_space} of {available_space} bytes available." + ) + return np.memmap(**kwargs) + + +class OnlineBuffer(torch.utils.data.Dataset): + """FIFO data buffer for the online training loop in train.py. + + Follows the protocol of LeRobotDataset as much as is required to have it be used by the online training + loop in the same way that a LeRobotDataset would be used. + + The underlying data structure will have data inserted in a circular fashion. Always insert after the + last index, and when you reach the end, wrap around to the start. + + The data is stored in a numpy memmap. + """ + + NEXT_INDEX_KEY = "_next_index" + OCCUPANCY_MASK_KEY = "_occupancy_mask" + INDEX_KEY = "index" + FRAME_INDEX_KEY = "frame_index" + EPISODE_INDEX_KEY = "episode_index" + TIMESTAMP_KEY = "timestamp" + IS_PAD_POSTFIX = "_is_pad" + + def __init__( + self, + write_dir: str | Path, + data_spec: dict[str, Any] | None, + buffer_capacity: int | None, + fps: float | None = None, + delta_timestamps: dict[str, list[float]] | dict[str, np.ndarray] | None = None, + ): + """ + The online buffer can be provided from scratch or you can load an existing online buffer by passing + a `write_dir` associated with an existing buffer. + + Args: + write_dir: Where to keep the numpy memmap files. One memmap file will be stored for each data key. + Note that if the files already exist, they are opened in read-write mode (used for training + resumption.) + data_spec: A mapping from data key to data specification, like {data_key: {"shape": tuple[int], + "dtype": np.dtype}}. This should include all the data that you wish to record into the buffer, + but note that "index", "frame_index" and "episode_index" are already accounted for by this + class, so you don't need to include them. + buffer_capacity: How many frames should be stored in the buffer as a maximum. Be aware of your + system's available disk space when choosing this. + fps: Same as the fps concept in LeRobot dataset. Here it needs to be provided for the + delta_timestamps logic. You can pass None if you are not using delta_timestamps. + delta_timestamps: Same as the delta_timestamps concept in LeRobotDataset. This is internally + converted to dict[str, np.ndarray] for optimization purposes. + + """ + self.set_delta_timestamps(delta_timestamps) + self._fps = fps + # Tolerance in seconds used to discard loaded frames when their timestamps are not close enough from + # the requested frames. It is only used when `delta_timestamps` is provided. + # minus 1e-4 to account for possible numerical error + self.tolerance_s = 1 / self.fps - 1e-4 if fps is not None else None + self._buffer_capacity = buffer_capacity + data_spec = self._make_data_spec(data_spec, buffer_capacity) + Path(write_dir).mkdir(parents=True, exist_ok=True) + self._data = {} + for k, v in data_spec.items(): + self._data[k] = _make_memmap_safe( + filename=Path(write_dir) / k, + dtype=v["dtype"] if v is not None else None, + mode="r+" if (Path(write_dir) / k).exists() else "w+", + shape=tuple(v["shape"]) if v is not None else None, + ) + + @property + def delta_timestamps(self) -> dict[str, np.ndarray] | None: + return self._delta_timestamps + + def set_delta_timestamps(self, value: dict[str, list[float]] | None): + """Set delta_timestamps converting the values to numpy arrays. + + The conversion is for an optimization in the __getitem__. The loop is much slower if the arrays + need to be converted into numpy arrays. + """ + if value is not None: + self._delta_timestamps = {k: np.array(v) for k, v in value.items()} + else: + self._delta_timestamps = None + + def _make_data_spec(self, data_spec: dict[str, Any], buffer_capacity: int) -> dict[str, dict[str, Any]]: + """Makes the data spec for np.memmap.""" + if any(k.startswith("_") for k in data_spec): + raise ValueError( + "data_spec keys should not start with '_'. This prefix is reserved for internal logic." + ) + preset_keys = { + OnlineBuffer.INDEX_KEY, + OnlineBuffer.FRAME_INDEX_KEY, + OnlineBuffer.EPISODE_INDEX_KEY, + OnlineBuffer.TIMESTAMP_KEY, + } + if len(intersection := set(data_spec).intersection(preset_keys)) > 0: + raise ValueError( + f"data_spec should not contain any of {preset_keys} as these are handled internally. " + f"The provided data_spec has {intersection}." + ) + complete_data_spec = { + # _next_index will be a pointer to the next index that we should start filling from when we add + # more data. + OnlineBuffer.NEXT_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": ()}, + # Since the memmap is initialized with all-zeros, this keeps track of which indices are occupied + # with real data rather than the dummy initialization. + OnlineBuffer.OCCUPANCY_MASK_KEY: {"dtype": np.dtype("?"), "shape": (buffer_capacity,)}, + OnlineBuffer.INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)}, + OnlineBuffer.FRAME_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)}, + OnlineBuffer.EPISODE_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)}, + OnlineBuffer.TIMESTAMP_KEY: {"dtype": np.dtype("float64"), "shape": (buffer_capacity,)}, + } + for k, v in data_spec.items(): + complete_data_spec[k] = {"dtype": v["dtype"], "shape": (buffer_capacity, *v["shape"])} + return complete_data_spec + + def add_data(self, data: dict[str, np.ndarray]): + """Add new data to the buffer, which could potentially mean shifting old data out. + + The new data should contain all the frames (in order) of any number of episodes. The indices should + start from 0 (note to the developer: this can easily be generalized). See the `rollout` and + `eval_policy` functions in `eval.py` for more information on how the data is constructed. + + Shift the incoming data index and episode_index to continue on from the last frame. Note that this + will be done in place! + """ + if len(missing_keys := (set(self.data_keys).difference(set(data)))) > 0: + raise ValueError(f"Missing data keys: {missing_keys}") + new_data_length = len(data[self.data_keys[0]]) + if not all(len(data[k]) == new_data_length for k in self.data_keys): + raise ValueError("All data items should have the same length") + + next_index = self._data[OnlineBuffer.NEXT_INDEX_KEY] + + # Sanity check to make sure that the new data indices start from 0. + assert data[OnlineBuffer.EPISODE_INDEX_KEY][0].item() == 0 + assert data[OnlineBuffer.INDEX_KEY][0].item() == 0 + + # Shift the incoming indices if necessary. + if self.num_frames > 0: + last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][next_index - 1] + last_data_index = self._data[OnlineBuffer.INDEX_KEY][next_index - 1] + data[OnlineBuffer.EPISODE_INDEX_KEY] += last_episode_index + 1 + data[OnlineBuffer.INDEX_KEY] += last_data_index + 1 + + # Insert the new data starting from next_index. It may be necessary to wrap around to the start. + n_surplus = max(0, new_data_length - (self._buffer_capacity - next_index)) + for k in self.data_keys: + if n_surplus == 0: + slc = slice(next_index, next_index + new_data_length) + self._data[k][slc] = data[k] + self._data[OnlineBuffer.OCCUPANCY_MASK_KEY][slc] = True + else: + self._data[k][next_index:] = data[k][:-n_surplus] + self._data[OnlineBuffer.OCCUPANCY_MASK_KEY][next_index:] = True + self._data[k][:n_surplus] = data[k][-n_surplus:] + if n_surplus == 0: + self._data[OnlineBuffer.NEXT_INDEX_KEY] = next_index + new_data_length + else: + self._data[OnlineBuffer.NEXT_INDEX_KEY] = n_surplus + + @property + def data_keys(self) -> list[str]: + keys = set(self._data) + keys.remove(OnlineBuffer.OCCUPANCY_MASK_KEY) + keys.remove(OnlineBuffer.NEXT_INDEX_KEY) + return sorted(keys) + + @property + def fps(self) -> float | None: + return self._fps + + @property + def num_episodes(self) -> int: + return len( + np.unique(self._data[OnlineBuffer.EPISODE_INDEX_KEY][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]]) + ) + + @property + def num_frames(self) -> int: + return np.count_nonzero(self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]) + + def __len__(self): + return self.num_frames + + def _item_to_tensors(self, item: dict) -> dict: + item_ = {} + for k, v in item.items(): + if isinstance(v, torch.Tensor): + item_[k] = v + elif isinstance(v, np.ndarray): + item_[k] = torch.from_numpy(v) + else: + item_[k] = torch.tensor(v) + return item_ + + def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: + if idx >= len(self) or idx < -len(self): + raise IndexError + + item = {k: v[idx] for k, v in self._data.items() if not k.startswith("_")} + + if self.delta_timestamps is None: + return self._item_to_tensors(item) + + episode_index = item[OnlineBuffer.EPISODE_INDEX_KEY] + current_ts = item[OnlineBuffer.TIMESTAMP_KEY] + episode_data_indices = np.where( + np.bitwise_and( + self._data[OnlineBuffer.EPISODE_INDEX_KEY] == episode_index, + self._data[OnlineBuffer.OCCUPANCY_MASK_KEY], + ) + )[0] + episode_timestamps = self._data[OnlineBuffer.TIMESTAMP_KEY][episode_data_indices] + + for data_key in self.delta_timestamps: + # Note: The logic in this loop is copied from `load_previous_and_future_frames`. + # Get timestamps used as query to retrieve data of previous/future frames. + query_ts = current_ts + self.delta_timestamps[data_key] + + # Compute distances between each query timestamp and all timestamps of all the frames belonging to + # the episode. + dist = np.abs(query_ts[:, None] - episode_timestamps[None, :]) + argmin_ = np.argmin(dist, axis=1) + min_ = dist[np.arange(dist.shape[0]), argmin_] + + is_pad = min_ > self.tolerance_s + + # Check violated query timestamps are all outside the episode range. + assert ( + (query_ts[is_pad] < episode_timestamps[0]) | (episode_timestamps[-1] < query_ts[is_pad]) + ).all(), ( + f"One or several timestamps unexpectedly violate the tolerance ({min_} > {self.tolerance_s=}" + ") inside the episode range." + ) + + # Load frames for this data key. + item[data_key] = self._data[data_key][episode_data_indices[argmin_]] + + item[f"{data_key}{OnlineBuffer.IS_PAD_POSTFIX}"] = is_pad + + return self._item_to_tensors(item) + + def get_data_by_key(self, key: str) -> torch.Tensor: + """Returns all data for a given data key as a Tensor.""" + return torch.from_numpy(self._data[key][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]]) + + +def compute_sampler_weights( + offline_dataset: LeRobotDataset, + offline_drop_n_last_frames: int = 0, + online_dataset: OnlineBuffer | None = None, + online_sampling_ratio: float | None = None, + online_drop_n_last_frames: int = 0, +) -> torch.Tensor: + """Compute the sampling weights for the online training dataloader in train.py. + + Args: + offline_dataset: The LeRobotDataset used for offline pre-training. + online_drop_n_last_frames: Number of frames to drop from the end of each offline dataset episode. + online_dataset: The OnlineBuffer used in online training. + online_sampling_ratio: The proportion of data that should be sampled from the online dataset. If an + online dataset is provided, this value must also be provided. + online_drop_n_first_frames: See `offline_drop_n_last_frames`. This is the same, but for the online + dataset. + Returns: + Tensor of weights for [offline_dataset; online_dataset], normalized to 1. + + Notes to maintainers: + - This duplicates some logic from EpisodeAwareSampler. We should consider converging to one approach. + - When used with `torch.utils.data.WeightedRandomSampler`, it could completely replace + `EpisodeAwareSampler` as the online dataset related arguments are optional. The only missing feature + is the ability to turn shuffling off. + - Options `drop_first_n_frames` and `episode_indices_to_use` can be added easily. They were not + included here to avoid adding complexity. + """ + if len(offline_dataset) == 0 and (online_dataset is None or len(online_dataset) == 0): + raise ValueError("At least one of `offline_dataset` or `online_dataset` should be contain data.") + if (online_dataset is None) ^ (online_sampling_ratio is None): + raise ValueError( + "`online_dataset` and `online_sampling_ratio` must be provided together or not at all." + ) + offline_sampling_ratio = 0 if online_sampling_ratio is None else 1 - online_sampling_ratio + + weights = [] + + if len(offline_dataset) > 0: + offline_data_mask_indices = [] + for start_index, end_index in zip( + offline_dataset.episode_data_index["from"], + offline_dataset.episode_data_index["to"], + strict=True, + ): + offline_data_mask_indices.extend( + range(start_index.item(), end_index.item() - offline_drop_n_last_frames) + ) + offline_data_mask = torch.zeros(len(offline_dataset), dtype=torch.bool) + offline_data_mask[torch.tensor(offline_data_mask_indices)] = True + weights.append( + torch.full( + size=(len(offline_dataset),), + fill_value=offline_sampling_ratio / offline_data_mask.sum(), + ) + * offline_data_mask + ) + + if online_dataset is not None and len(online_dataset) > 0: + online_data_mask_indices = [] + episode_indices = online_dataset.get_data_by_key("episode_index") + for episode_idx in torch.unique(episode_indices): + where_episode = torch.where(episode_indices == episode_idx) + start_index = where_episode[0][0] + end_index = where_episode[0][-1] + 1 + online_data_mask_indices.extend( + range(start_index.item(), end_index.item() - online_drop_n_last_frames) + ) + online_data_mask = torch.zeros(len(online_dataset), dtype=torch.bool) + online_data_mask[torch.tensor(online_data_mask_indices)] = True + weights.append( + torch.full( + size=(len(online_dataset),), + fill_value=online_sampling_ratio / online_data_mask.sum(), + ) + * online_data_mask + ) + + weights = torch.cat(weights) + + if weights.sum() == 0: + weights += 1 / len(weights) + else: + weights /= weights.sum() + + return weights diff --git a/lerobot/common/datasets/push_dataset_to_hub/utils.py b/lerobot/common/datasets/push_dataset_to_hub/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9563f0e68d59267a95cfec76306eea65910c5219 --- /dev/null +++ b/lerobot/common/datasets/push_dataset_to_hub/utils.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import inspect +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +from typing import Dict + +import datasets +import numpy +import PIL +import torch + +from lerobot.common.datasets.video_utils import encode_video_frames + + +def concatenate_episodes(ep_dicts): + data_dict = {} + + keys = ep_dicts[0].keys() + for key in keys: + if torch.is_tensor(ep_dicts[0][key][0]): + data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts]) + else: + if key not in data_dict: + data_dict[key] = [] + for ep_dict in ep_dicts: + for x in ep_dict[key]: + data_dict[key].append(x) + + total_frames = data_dict["frame_index"].shape[0] + data_dict["index"] = torch.arange(0, total_frames, 1) + return data_dict + + +def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4): + out_dir = Path(out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + def save_image(img_array, i, out_dir): + img = PIL.Image.fromarray(img_array) + img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100) + + num_images = len(imgs_array) + with ThreadPoolExecutor(max_workers=max_workers) as executor: + [executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)] + + +def get_default_encoding() -> dict: + """Returns the default ffmpeg encoding parameters used by `encode_video_frames`.""" + signature = inspect.signature(encode_video_frames) + return { + k: v.default + for k, v in signature.parameters.items() + if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"] + } + + +def check_repo_id(repo_id: str) -> None: + if len(repo_id.split("/")) != 2: + raise ValueError( + f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset + (e.g. 'lerobot/pusht'), but contains '{repo_id}'.""" + ) + + +# TODO(aliberts): remove +def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]: + """ + Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset. + + Parameters: + - hf_dataset (datasets.Dataset): A HuggingFace dataset containing the episode index. + + Returns: + - episode_data_index: A dictionary containing the data index for each episode. The dictionary has two keys: + - "from": A tensor containing the starting index of each episode. + - "to": A tensor containing the ending index of each episode. + """ + episode_data_index = {"from": [], "to": []} + + current_episode = None + """ + The episode_index is a list of integers, each representing the episode index of the corresponding example. + For instance, the following is a valid episode_index: + [0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2] + + Below, we iterate through the episode_index and populate the episode_data_index dictionary with the starting and + ending index of each episode. For the episode_index above, the episode_data_index dictionary will look like this: + { + "from": [0, 3, 7], + "to": [3, 7, 12] + } + """ + if len(hf_dataset) == 0: + episode_data_index = { + "from": torch.tensor([]), + "to": torch.tensor([]), + } + return episode_data_index + for idx, episode_idx in enumerate(hf_dataset["episode_index"]): + if episode_idx != current_episode: + # We encountered a new episode, so we append its starting location to the "from" list + episode_data_index["from"].append(idx) + # If this is not the first episode, we append the ending location of the previous episode to the "to" list + if current_episode is not None: + episode_data_index["to"].append(idx) + # Let's keep track of the current episode index + current_episode = episode_idx + else: + # We are still in the same episode, so there is nothing for us to do here + pass + # We have reached the end of the dataset, so we append the ending location of the last episode to the "to" list + episode_data_index["to"].append(idx + 1) + + for k in ["from", "to"]: + episode_data_index[k] = torch.tensor(episode_data_index[k]) + + return episode_data_index diff --git a/lerobot/common/datasets/sampler.py b/lerobot/common/datasets/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..0ea24bad5ffae64f299e6281db87d09d0e6fc68e --- /dev/null +++ b/lerobot/common/datasets/sampler.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Iterator, Union + +import torch + + +class EpisodeAwareSampler: + def __init__( + self, + episode_data_index: dict, + episode_indices_to_use: Union[list, None] = None, + drop_n_first_frames: int = 0, + drop_n_last_frames: int = 0, + shuffle: bool = False, + ): + """Sampler that optionally incorporates episode boundary information. + + Args: + episode_data_index: Dictionary with keys 'from' and 'to' containing the start and end indices of each episode. + episode_indices_to_use: List of episode indices to use. If None, all episodes are used. + Assumes that episodes are indexed from 0 to N-1. + drop_n_first_frames: Number of frames to drop from the start of each episode. + drop_n_last_frames: Number of frames to drop from the end of each episode. + shuffle: Whether to shuffle the indices. + """ + indices = [] + for episode_idx, (start_index, end_index) in enumerate( + zip(episode_data_index["from"], episode_data_index["to"], strict=True) + ): + if episode_indices_to_use is None or episode_idx in episode_indices_to_use: + indices.extend( + range(start_index.item() + drop_n_first_frames, end_index.item() - drop_n_last_frames) + ) + + self.indices = indices + self.shuffle = shuffle + + def __iter__(self) -> Iterator[int]: + if self.shuffle: + for i in torch.randperm(len(self.indices)): + yield self.indices[i] + else: + for i in self.indices: + yield i + + def __len__(self) -> int: + return len(self.indices) diff --git a/lerobot/common/datasets/transforms.py b/lerobot/common/datasets/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..5a3a6cfb31747243b70aab2e5e8c3e78c809b6f6 --- /dev/null +++ b/lerobot/common/datasets/transforms.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import collections +from dataclasses import dataclass, field +from typing import Any, Callable, Sequence + +import torch +from torchvision.transforms import v2 +from torchvision.transforms.v2 import Transform +from torchvision.transforms.v2 import functional as F # noqa: N812 + + +class RandomSubsetApply(Transform): + """Apply a random subset of N transformations from a list of transformations. + + Args: + transforms: list of transformations. + p: represents the multinomial probabilities (with no replacement) used for sampling the transform. + If the sum of the weights is not 1, they will be normalized. If ``None`` (default), all transforms + have the same probability. + n_subset: number of transformations to apply. If ``None``, all transforms are applied. + Must be in [1, len(transforms)]. + random_order: apply transformations in a random order. + """ + + def __init__( + self, + transforms: Sequence[Callable], + p: list[float] | None = None, + n_subset: int | None = None, + random_order: bool = False, + ) -> None: + super().__init__() + if not isinstance(transforms, Sequence): + raise TypeError("Argument transforms should be a sequence of callables") + if p is None: + p = [1] * len(transforms) + elif len(p) != len(transforms): + raise ValueError( + f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}" + ) + + if n_subset is None: + n_subset = len(transforms) + elif not isinstance(n_subset, int): + raise TypeError("n_subset should be an int or None") + elif not (1 <= n_subset <= len(transforms)): + raise ValueError(f"n_subset should be in the interval [1, {len(transforms)}]") + + self.transforms = transforms + total = sum(p) + self.p = [prob / total for prob in p] + self.n_subset = n_subset + self.random_order = random_order + + self.selected_transforms = None + + def forward(self, *inputs: Any) -> Any: + needs_unpacking = len(inputs) > 1 + + selected_indices = torch.multinomial(torch.tensor(self.p), self.n_subset) + if not self.random_order: + selected_indices = selected_indices.sort().values + + self.selected_transforms = [self.transforms[i] for i in selected_indices] + + for transform in self.selected_transforms: + outputs = transform(*inputs) + inputs = outputs if needs_unpacking else (outputs,) + + return outputs + + def extra_repr(self) -> str: + return ( + f"transforms={self.transforms}, " + f"p={self.p}, " + f"n_subset={self.n_subset}, " + f"random_order={self.random_order}" + ) + + +class SharpnessJitter(Transform): + """Randomly change the sharpness of an image or video. + + Similar to a v2.RandomAdjustSharpness with p=1 and a sharpness_factor sampled randomly. + While v2.RandomAdjustSharpness applies — with a given probability — a fixed sharpness_factor to an image, + SharpnessJitter applies a random sharpness_factor each time. This is to have a more diverse set of + augmentations as a result. + + A sharpness_factor of 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness + by a factor of 2. + + If the input is a :class:`torch.Tensor`, + it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + sharpness: How much to jitter sharpness. sharpness_factor is chosen uniformly from + [max(0, 1 - sharpness), 1 + sharpness] or the given + [min, max]. Should be non negative numbers. + """ + + def __init__(self, sharpness: float | Sequence[float]) -> None: + super().__init__() + self.sharpness = self._check_input(sharpness) + + def _check_input(self, sharpness): + if isinstance(sharpness, (int, float)): + if sharpness < 0: + raise ValueError("If sharpness is a single number, it must be non negative.") + sharpness = [1.0 - sharpness, 1.0 + sharpness] + sharpness[0] = max(sharpness[0], 0.0) + elif isinstance(sharpness, collections.abc.Sequence) and len(sharpness) == 2: + sharpness = [float(v) for v in sharpness] + else: + raise TypeError(f"{sharpness=} should be a single number or a sequence with length 2.") + + if not 0.0 <= sharpness[0] <= sharpness[1]: + raise ValueError(f"sharpness values should be between (0., inf), but got {sharpness}.") + + return float(sharpness[0]), float(sharpness[1]) + + def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]: + sharpness_factor = torch.empty(1).uniform_(self.sharpness[0], self.sharpness[1]).item() + return {"sharpness_factor": sharpness_factor} + + def transform(self, inpt: Any, params: dict[str, Any]) -> Any: + sharpness_factor = params["sharpness_factor"] + return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor) + + +@dataclass +class ImageTransformConfig: + """ + For each transform, the following parameters are available: + weight: This represents the multinomial probability (with no replacement) + used for sampling the transform. If the sum of the weights is not 1, + they will be normalized. + type: The name of the class used. This is either a class available under torchvision.transforms.v2 or a + custom transform defined here. + kwargs: Lower & upper bound respectively used for sampling the transform's parameter + (following uniform distribution) when it's applied. + """ + + weight: float = 1.0 + type: str = "Identity" + kwargs: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class ImageTransformsConfig: + """ + These transforms are all using standard torchvision.transforms.v2 + You can find out how these transformations affect images here: + https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html + We use a custom RandomSubsetApply container to sample them. + """ + + # Set this flag to `true` to enable transforms during training + enable: bool = False + # This is the maximum number of transforms (sampled from these below) that will be applied to each frame. + # It's an integer in the interval [1, number_of_available_transforms]. + max_num_transforms: int = 3 + # By default, transforms are applied in Torchvision's suggested order (shown below). + # Set this to True to apply them in a random order. + random_order: bool = False + tfs: dict[str, ImageTransformConfig] = field( + default_factory=lambda: { + "brightness": ImageTransformConfig( + weight=1.0, + type="ColorJitter", + kwargs={"brightness": (0.8, 1.2)}, + ), + "contrast": ImageTransformConfig( + weight=1.0, + type="ColorJitter", + kwargs={"contrast": (0.8, 1.2)}, + ), + "saturation": ImageTransformConfig( + weight=1.0, + type="ColorJitter", + kwargs={"saturation": (0.5, 1.5)}, + ), + "hue": ImageTransformConfig( + weight=1.0, + type="ColorJitter", + kwargs={"hue": (-0.05, 0.05)}, + ), + "sharpness": ImageTransformConfig( + weight=1.0, + type="SharpnessJitter", + kwargs={"sharpness": (0.5, 1.5)}, + ), + } + ) + + +def make_transform_from_config(cfg: ImageTransformConfig): + if cfg.type == "Identity": + return v2.Identity(**cfg.kwargs) + elif cfg.type == "ColorJitter": + return v2.ColorJitter(**cfg.kwargs) + elif cfg.type == "SharpnessJitter": + return SharpnessJitter(**cfg.kwargs) + else: + raise ValueError(f"Transform '{cfg.type}' is not valid.") + + +class ImageTransforms(Transform): + """A class to compose image transforms based on configuration.""" + + def __init__(self, cfg: ImageTransformsConfig) -> None: + super().__init__() + self._cfg = cfg + + self.weights = [] + self.transforms = {} + for tf_name, tf_cfg in cfg.tfs.items(): + if tf_cfg.weight <= 0.0: + continue + + self.transforms[tf_name] = make_transform_from_config(tf_cfg) + self.weights.append(tf_cfg.weight) + + n_subset = min(len(self.transforms), cfg.max_num_transforms) + if n_subset == 0 or not cfg.enable: + self.tf = v2.Identity() + else: + self.tf = RandomSubsetApply( + transforms=list(self.transforms.values()), + p=self.weights, + n_subset=n_subset, + random_order=cfg.random_order, + ) + + def forward(self, *inputs: Any) -> Any: + return self.tf(*inputs) diff --git a/lerobot/common/datasets/utils.py b/lerobot/common/datasets/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c054071dccf7beedb91b8b1cf7c26118b5f0d340 --- /dev/null +++ b/lerobot/common/datasets/utils.py @@ -0,0 +1,860 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import contextlib +import importlib.resources +import json +import logging +from collections.abc import Iterator +from itertools import accumulate +from pathlib import Path +from pprint import pformat +from types import SimpleNamespace +from typing import Any + +import datasets +import jsonlines +import numpy as np +import packaging.version +import torch +from datasets.table import embed_table_storage +from huggingface_hub import DatasetCard, DatasetCardData, HfApi +from huggingface_hub.errors import RevisionNotFoundError +from PIL import Image as PILImage +from torchvision import transforms + +from lerobot.common.datasets.backward_compatibility import ( + V21_MESSAGE, + BackwardCompatibilityError, + ForwardCompatibilityError, +) +from lerobot.common.robots import Robot +from lerobot.common.utils.utils import is_valid_numpy_dtype_string +from lerobot.configs.types import DictLike, FeatureType, PolicyFeature + +DEFAULT_CHUNK_SIZE = 1000 # Max number of episodes per chunk + +INFO_PATH = "meta/info.json" +EPISODES_PATH = "meta/episodes.jsonl" +STATS_PATH = "meta/stats.json" +EPISODES_STATS_PATH = "meta/episodes_stats.jsonl" +TASKS_PATH = "meta/tasks.jsonl" + +DEFAULT_VIDEO_PATH = "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4" +DEFAULT_PARQUET_PATH = "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet" +DEFAULT_IMAGE_PATH = "images/{image_key}/episode_{episode_index:06d}/frame_{frame_index:06d}.png" + +DATASET_CARD_TEMPLATE = """ +--- +# Metadata will go there +--- +This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). + +## {} + +""" + +DEFAULT_FEATURES = { + "timestamp": {"dtype": "float32", "shape": (1,), "names": None}, + "frame_index": {"dtype": "int64", "shape": (1,), "names": None}, + "episode_index": {"dtype": "int64", "shape": (1,), "names": None}, + "index": {"dtype": "int64", "shape": (1,), "names": None}, + "task_index": {"dtype": "int64", "shape": (1,), "names": None}, +} + + +def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict: + """Flatten a nested dictionary structure by collapsing nested keys into one key with a separator. + + For example: + ``` + >>> dct = {"a": {"b": 1, "c": {"d": 2}}, "e": 3}` + >>> print(flatten_dict(dct)) + {"a/b": 1, "a/c/d": 2, "e": 3} + """ + items = [] + for k, v in d.items(): + new_key = f"{parent_key}{sep}{k}" if parent_key else k + if isinstance(v, dict): + items.extend(flatten_dict(v, new_key, sep=sep).items()) + else: + items.append((new_key, v)) + return dict(items) + + +def unflatten_dict(d: dict, sep: str = "/") -> dict: + outdict = {} + for key, value in d.items(): + parts = key.split(sep) + d = outdict + for part in parts[:-1]: + if part not in d: + d[part] = {} + d = d[part] + d[parts[-1]] = value + return outdict + + +def get_nested_item(obj: DictLike, flattened_key: str, sep: str = "/") -> Any: + split_keys = flattened_key.split(sep) + getter = obj[split_keys[0]] + if len(split_keys) == 1: + return getter + + for key in split_keys[1:]: + getter = getter[key] + + return getter + + +def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict: + serialized_dict = {} + for key, value in flatten_dict(stats).items(): + if isinstance(value, (torch.Tensor, np.ndarray)): + serialized_dict[key] = value.tolist() + elif isinstance(value, np.generic): + serialized_dict[key] = value.item() + elif isinstance(value, (int, float)): + serialized_dict[key] = value + else: + raise NotImplementedError(f"The value '{value}' of type '{type(value)}' is not supported.") + return unflatten_dict(serialized_dict) + + +def embed_images(dataset: datasets.Dataset) -> datasets.Dataset: + # Embed image bytes into the table before saving to parquet + format = dataset.format + dataset = dataset.with_format("arrow") + dataset = dataset.map(embed_table_storage, batched=False) + dataset = dataset.with_format(**format) + return dataset + + +def load_json(fpath: Path) -> Any: + with open(fpath) as f: + return json.load(f) + + +def write_json(data: dict, fpath: Path) -> None: + fpath.parent.mkdir(exist_ok=True, parents=True) + with open(fpath, "w") as f: + json.dump(data, f, indent=4, ensure_ascii=False) + + +def load_jsonlines(fpath: Path) -> list[Any]: + with jsonlines.open(fpath, "r") as reader: + return list(reader) + + +def write_jsonlines(data: dict, fpath: Path) -> None: + fpath.parent.mkdir(exist_ok=True, parents=True) + with jsonlines.open(fpath, "w") as writer: + writer.write_all(data) + + +def append_jsonlines(data: dict, fpath: Path) -> None: + fpath.parent.mkdir(exist_ok=True, parents=True) + with jsonlines.open(fpath, "a") as writer: + writer.write(data) + + +def write_info(info: dict, local_dir: Path): + write_json(info, local_dir / INFO_PATH) + + +def load_info(local_dir: Path) -> dict: + info = load_json(local_dir / INFO_PATH) + for ft in info["features"].values(): + ft["shape"] = tuple(ft["shape"]) + return info + + +def write_stats(stats: dict, local_dir: Path): + serialized_stats = serialize_dict(stats) + write_json(serialized_stats, local_dir / STATS_PATH) + + +def cast_stats_to_numpy(stats) -> dict[str, dict[str, np.ndarray]]: + stats = {key: np.array(value) for key, value in flatten_dict(stats).items()} + return unflatten_dict(stats) + + +def load_stats(local_dir: Path) -> dict[str, dict[str, np.ndarray]]: + if not (local_dir / STATS_PATH).exists(): + return None + stats = load_json(local_dir / STATS_PATH) + return cast_stats_to_numpy(stats) + + +def write_task(task_index: int, task: dict, local_dir: Path): + task_dict = { + "task_index": task_index, + "task": task, + } + append_jsonlines(task_dict, local_dir / TASKS_PATH) + + +def load_tasks(local_dir: Path) -> tuple[dict, dict]: + tasks = load_jsonlines(local_dir / TASKS_PATH) + tasks = {item["task_index"]: item["task"] for item in sorted(tasks, key=lambda x: x["task_index"])} + task_to_task_index = {task: task_index for task_index, task in tasks.items()} + return tasks, task_to_task_index + + +def write_episode(episode: dict, local_dir: Path): + append_jsonlines(episode, local_dir / EPISODES_PATH) + + +def load_episodes(local_dir: Path) -> dict: + episodes = load_jsonlines(local_dir / EPISODES_PATH) + return {item["episode_index"]: item for item in sorted(episodes, key=lambda x: x["episode_index"])} + + +def write_episode_stats(episode_index: int, episode_stats: dict, local_dir: Path): + # We wrap episode_stats in a dictionary since `episode_stats["episode_index"]` + # is a dictionary of stats and not an integer. + episode_stats = {"episode_index": episode_index, "stats": serialize_dict(episode_stats)} + append_jsonlines(episode_stats, local_dir / EPISODES_STATS_PATH) + + +def load_episodes_stats(local_dir: Path) -> dict: + episodes_stats = load_jsonlines(local_dir / EPISODES_STATS_PATH) + return { + item["episode_index"]: cast_stats_to_numpy(item["stats"]) + for item in sorted(episodes_stats, key=lambda x: x["episode_index"]) + } + + +def backward_compatible_episodes_stats( + stats: dict[str, dict[str, np.ndarray]], episodes: list[int] +) -> dict[str, dict[str, np.ndarray]]: + return dict.fromkeys(episodes, stats) + + +def load_image_as_numpy( + fpath: str | Path, dtype: np.dtype = np.float32, channel_first: bool = True +) -> np.ndarray: + img = PILImage.open(fpath).convert("RGB") + img_array = np.array(img, dtype=dtype) + if channel_first: # (H, W, C) -> (C, H, W) + img_array = np.transpose(img_array, (2, 0, 1)) + if np.issubdtype(dtype, np.floating): + img_array /= 255.0 + return img_array + + +def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]): + """Get a transform function that convert items from Hugging Face dataset (pyarrow) + to torch tensors. Importantly, images are converted from PIL, which corresponds to + a channel last representation (h w c) of uint8 type, to a torch image representation + with channel first (c h w) of float32 type in range [0,1]. + """ + for key in items_dict: + first_item = items_dict[key][0] + if isinstance(first_item, PILImage.Image): + to_tensor = transforms.ToTensor() + items_dict[key] = [to_tensor(img) for img in items_dict[key]] + elif first_item is None: + pass + else: + items_dict[key] = [x if isinstance(x, str) else torch.tensor(x) for x in items_dict[key]] + return items_dict + + +def is_valid_version(version: str) -> bool: + try: + packaging.version.parse(version) + return True + except packaging.version.InvalidVersion: + return False + + +def check_version_compatibility( + repo_id: str, + version_to_check: str | packaging.version.Version, + current_version: str | packaging.version.Version, + enforce_breaking_major: bool = True, +) -> None: + v_check = ( + packaging.version.parse(version_to_check) + if not isinstance(version_to_check, packaging.version.Version) + else version_to_check + ) + v_current = ( + packaging.version.parse(current_version) + if not isinstance(current_version, packaging.version.Version) + else current_version + ) + if v_check.major < v_current.major and enforce_breaking_major: + raise BackwardCompatibilityError(repo_id, v_check) + elif v_check.minor < v_current.minor: + logging.warning(V21_MESSAGE.format(repo_id=repo_id, version=v_check)) + + +def get_repo_versions(repo_id: str) -> list[packaging.version.Version]: + """Returns available valid versions (branches and tags) on given repo.""" + api = HfApi() + repo_refs = api.list_repo_refs(repo_id, repo_type="dataset") + repo_refs = [b.name for b in repo_refs.branches + repo_refs.tags] + repo_versions = [] + for ref in repo_refs: + with contextlib.suppress(packaging.version.InvalidVersion): + repo_versions.append(packaging.version.parse(ref)) + + return repo_versions + + +def get_safe_version(repo_id: str, version: str | packaging.version.Version) -> str: + """ + Returns the version if available on repo or the latest compatible one. + Otherwise, will throw a `CompatibilityError`. + """ + target_version = ( + packaging.version.parse(version) if not isinstance(version, packaging.version.Version) else version + ) + hub_versions = get_repo_versions(repo_id) + + if not hub_versions: + raise RevisionNotFoundError( + f"""Your dataset must be tagged with a codebase version. + Assuming _version_ is the codebase_version value in the info.json, you can run this: + ```python + from huggingface_hub import HfApi + + hub_api = HfApi() + hub_api.create_tag("{repo_id}", tag="_version_", repo_type="dataset") + ``` + """ + ) + + if target_version in hub_versions: + return f"v{target_version}" + + compatibles = [ + v for v in hub_versions if v.major == target_version.major and v.minor <= target_version.minor + ] + if compatibles: + return_version = max(compatibles) + if return_version < target_version: + logging.warning(f"Revision {version} for {repo_id} not found, using version v{return_version}") + return f"v{return_version}" + + lower_major = [v for v in hub_versions if v.major < target_version.major] + if lower_major: + raise BackwardCompatibilityError(repo_id, max(lower_major)) + + upper_versions = [v for v in hub_versions if v > target_version] + assert len(upper_versions) > 0 + raise ForwardCompatibilityError(repo_id, min(upper_versions)) + + +def get_hf_features_from_features(features: dict) -> datasets.Features: + hf_features = {} + for key, ft in features.items(): + if ft["dtype"] == "video": + continue + elif ft["dtype"] == "image": + hf_features[key] = datasets.Image() + elif ft["shape"] == (1,): + hf_features[key] = datasets.Value(dtype=ft["dtype"]) + elif len(ft["shape"]) == 1: + hf_features[key] = datasets.Sequence( + length=ft["shape"][0], feature=datasets.Value(dtype=ft["dtype"]) + ) + elif len(ft["shape"]) == 2: + hf_features[key] = datasets.Array2D(shape=ft["shape"], dtype=ft["dtype"]) + elif len(ft["shape"]) == 3: + hf_features[key] = datasets.Array3D(shape=ft["shape"], dtype=ft["dtype"]) + elif len(ft["shape"]) == 4: + hf_features[key] = datasets.Array4D(shape=ft["shape"], dtype=ft["dtype"]) + elif len(ft["shape"]) == 5: + hf_features[key] = datasets.Array5D(shape=ft["shape"], dtype=ft["dtype"]) + else: + raise ValueError(f"Corresponding feature is not valid: {ft}") + + return datasets.Features(hf_features) + + +def _validate_feature_names(features: dict[str, dict]) -> None: + invalid_features = {name: ft for name, ft in features.items() if "/" in name} + if invalid_features: + raise ValueError(f"Feature names should not contain '/'. Found '/' in '{invalid_features}'.") + + +def hw_to_dataset_features( + hw_features: dict[str, type | tuple], prefix: str, use_video: bool = True +) -> dict[str, dict]: + features = {} + joint_fts = {key: ftype for key, ftype in hw_features.items() if ftype is float} + cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)} + + if joint_fts and prefix == "action": + features[prefix] = { + "dtype": "float32", + "shape": (len(joint_fts),), + "names": list(joint_fts), + } + + if joint_fts and prefix == "observation": + features[f"{prefix}.state"] = { + "dtype": "float32", + "shape": (len(joint_fts),), + "names": list(joint_fts), + } + + for key, shape in cam_fts.items(): + features[f"{prefix}.images.{key}"] = { + "dtype": "video" if use_video else "image", + "shape": shape, + "names": ["height", "width", "channels"], + } + + _validate_feature_names(features) + return features + + +def build_dataset_frame( + ds_features: dict[str, dict], values: dict[str, Any], prefix: str +) -> dict[str, np.ndarray]: + frame = {} + for key, ft in ds_features.items(): + if key in DEFAULT_FEATURES or not key.startswith(prefix): + continue + elif ft["dtype"] == "float32" and len(ft["shape"]) == 1: + frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32) + elif ft["dtype"] in ["image", "video"]: + frame[key] = values[key.removeprefix(f"{prefix}.images.")] + + return frame + + +def get_features_from_robot(robot: Robot, use_videos: bool = True) -> dict: + camera_ft = {} + if robot.cameras: + camera_ft = { + key: {"dtype": "video" if use_videos else "image", **ft} + for key, ft in robot.camera_features.items() + } + return {**robot.motor_features, **camera_ft, **DEFAULT_FEATURES} + + +def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFeature]: + # TODO(aliberts): Implement "type" in dataset features and simplify this + policy_features = {} + for key, ft in features.items(): + shape = ft["shape"] + if ft["dtype"] in ["image", "video"]: + type = FeatureType.VISUAL + if len(shape) != 3: + raise ValueError(f"Number of dimensions of {key} != 3 (shape={shape})") + + names = ft["names"] + # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets. + if names[2] in ["channel", "channels"]: # (h, w, c) -> (c, h, w) + shape = (shape[2], shape[0], shape[1]) + elif key == "observation.environment_state": + type = FeatureType.ENV + elif key.startswith("observation"): + type = FeatureType.STATE + elif key.startswith("action"): + type = FeatureType.ACTION + else: + continue + + policy_features[key] = PolicyFeature( + type=type, + shape=shape, + ) + + return policy_features + + +def create_empty_dataset_info( + codebase_version: str, + fps: int, + features: dict, + use_videos: bool, + robot_type: str | None = None, +) -> dict: + return { + "codebase_version": codebase_version, + "robot_type": robot_type, + "total_episodes": 0, + "total_frames": 0, + "total_tasks": 0, + "total_videos": 0, + "total_chunks": 0, + "chunks_size": DEFAULT_CHUNK_SIZE, + "fps": fps, + "splits": {}, + "data_path": DEFAULT_PARQUET_PATH, + "video_path": DEFAULT_VIDEO_PATH if use_videos else None, + "features": features, + } + + +def get_episode_data_index( + episode_dicts: dict[dict], episodes: list[int] | None = None +) -> dict[str, torch.Tensor]: + episode_lengths = {ep_idx: ep_dict["length"] for ep_idx, ep_dict in episode_dicts.items()} + if episodes is not None: + episode_lengths = {ep_idx: episode_lengths[ep_idx] for ep_idx in episodes} + + cumulative_lengths = list(accumulate(episode_lengths.values())) + return { + "from": torch.LongTensor([0] + cumulative_lengths[:-1]), + "to": torch.LongTensor(cumulative_lengths), + } + + +def check_timestamps_sync( + timestamps: np.ndarray, + episode_indices: np.ndarray, + episode_data_index: dict[str, np.ndarray], + fps: int, + tolerance_s: float, + raise_value_error: bool = True, +) -> bool: + """ + This check is to make sure that each timestamp is separated from the next by (1/fps) +/- tolerance + to account for possible numerical error. + + Args: + timestamps (np.ndarray): Array of timestamps in seconds. + episode_indices (np.ndarray): Array indicating the episode index for each timestamp. + episode_data_index (dict[str, np.ndarray]): A dictionary that includes 'to', + which identifies indices for the end of each episode. + fps (int): Frames per second. Used to check the expected difference between consecutive timestamps. + tolerance_s (float): Allowed deviation from the expected (1/fps) difference. + raise_value_error (bool): Whether to raise a ValueError if the check fails. + + Returns: + bool: True if all checked timestamp differences lie within tolerance, False otherwise. + + Raises: + ValueError: If the check fails and `raise_value_error` is True. + """ + if timestamps.shape != episode_indices.shape: + raise ValueError( + "timestamps and episode_indices should have the same shape. " + f"Found {timestamps.shape=} and {episode_indices.shape=}." + ) + + # Consecutive differences + diffs = np.diff(timestamps) + within_tolerance = np.abs(diffs - (1.0 / fps)) <= tolerance_s + + # Mask to ignore differences at the boundaries between episodes + mask = np.ones(len(diffs), dtype=bool) + ignored_diffs = episode_data_index["to"][:-1] - 1 # indices at the end of each episode + mask[ignored_diffs] = False + filtered_within_tolerance = within_tolerance[mask] + + # Check if all remaining diffs are within tolerance + if not np.all(filtered_within_tolerance): + # Track original indices before masking + original_indices = np.arange(len(diffs)) + filtered_indices = original_indices[mask] + outside_tolerance_filtered_indices = np.nonzero(~filtered_within_tolerance)[0] + outside_tolerance_indices = filtered_indices[outside_tolerance_filtered_indices] + + outside_tolerances = [] + for idx in outside_tolerance_indices: + entry = { + "timestamps": [timestamps[idx], timestamps[idx + 1]], + "diff": diffs[idx], + "episode_index": episode_indices[idx].item() + if hasattr(episode_indices[idx], "item") + else episode_indices[idx], + } + outside_tolerances.append(entry) + + if raise_value_error: + raise ValueError( + f"""One or several timestamps unexpectedly violate the tolerance inside episode range. + This might be due to synchronization issues during data collection. + \n{pformat(outside_tolerances)}""" + ) + return False + + return True + + +def check_delta_timestamps( + delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True +) -> bool: + """This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance. + This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be + actual timestamps from the dataset. + """ + outside_tolerance = {} + for key, delta_ts in delta_timestamps.items(): + within_tolerance = [abs(ts * fps - round(ts * fps)) / fps <= tolerance_s for ts in delta_ts] + if not all(within_tolerance): + outside_tolerance[key] = [ + ts for ts, is_within in zip(delta_ts, within_tolerance, strict=True) if not is_within + ] + + if len(outside_tolerance) > 0: + if raise_value_error: + raise ValueError( + f""" + The following delta_timestamps are found outside of tolerance range. + Please make sure they are multiples of 1/{fps} +/- tolerance and adjust + their values accordingly. + \n{pformat(outside_tolerance)} + """ + ) + return False + + return True + + +def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]: + delta_indices = {} + for key, delta_ts in delta_timestamps.items(): + delta_indices[key] = [round(d * fps) for d in delta_ts] + + return delta_indices + + +def cycle(iterable): + """The equivalent of itertools.cycle, but safe for Pytorch dataloaders. + + See https://github.com/pytorch/pytorch/issues/23900 for information on why itertools.cycle is not safe. + """ + iterator = iter(iterable) + while True: + try: + yield next(iterator) + except StopIteration: + iterator = iter(iterable) + + +def create_branch(repo_id, *, branch: str, repo_type: str | None = None) -> None: + """Create a branch on a existing Hugging Face repo. Delete the branch if it already + exists before creating it. + """ + api = HfApi() + + branches = api.list_repo_refs(repo_id, repo_type=repo_type).branches + refs = [branch.ref for branch in branches] + ref = f"refs/heads/{branch}" + if ref in refs: + api.delete_branch(repo_id, repo_type=repo_type, branch=branch) + + api.create_branch(repo_id, repo_type=repo_type, branch=branch) + + +def create_lerobot_dataset_card( + tags: list | None = None, + dataset_info: dict | None = None, + **kwargs, +) -> DatasetCard: + """ + Keyword arguments will be used to replace values in ./lerobot/common/datasets/card_template.md. + Note: If specified, license must be one of https://huggingface.co/docs/hub/repositories-licenses. + """ + card_tags = ["LeRobot"] + + if tags: + card_tags += tags + if dataset_info: + dataset_structure = "[meta/info.json](meta/info.json):\n" + dataset_structure += f"```json\n{json.dumps(dataset_info, indent=4)}\n```\n" + kwargs = {**kwargs, "dataset_structure": dataset_structure} + card_data = DatasetCardData( + license=kwargs.get("license"), + tags=card_tags, + task_categories=["robotics"], + configs=[ + { + "config_name": "default", + "data_files": "data/*/*.parquet", + } + ], + ) + + card_template = (importlib.resources.files("lerobot.common.datasets") / "card_template.md").read_text() + + return DatasetCard.from_template( + card_data=card_data, + template_str=card_template, + **kwargs, + ) + + +class IterableNamespace(SimpleNamespace): + """ + A namespace object that supports both dictionary-like iteration and dot notation access. + Automatically converts nested dictionaries into IterableNamespaces. + + This class extends SimpleNamespace to provide: + - Dictionary-style iteration over keys + - Access to items via both dot notation (obj.key) and brackets (obj["key"]) + - Dictionary-like methods: items(), keys(), values() + - Recursive conversion of nested dictionaries + + Args: + dictionary: Optional dictionary to initialize the namespace + **kwargs: Additional keyword arguments passed to SimpleNamespace + + Examples: + >>> data = {"name": "Alice", "details": {"age": 25}} + >>> ns = IterableNamespace(data) + >>> ns.name + 'Alice' + >>> ns.details.age + 25 + >>> list(ns.keys()) + ['name', 'details'] + >>> for key, value in ns.items(): + ... print(f"{key}: {value}") + name: Alice + details: IterableNamespace(age=25) + """ + + def __init__(self, dictionary: dict[str, Any] = None, **kwargs): + super().__init__(**kwargs) + if dictionary is not None: + for key, value in dictionary.items(): + if isinstance(value, dict): + setattr(self, key, IterableNamespace(value)) + else: + setattr(self, key, value) + + def __iter__(self) -> Iterator[str]: + return iter(vars(self)) + + def __getitem__(self, key: str) -> Any: + return vars(self)[key] + + def items(self): + return vars(self).items() + + def values(self): + return vars(self).values() + + def keys(self): + return vars(self).keys() + + +def validate_frame(frame: dict, features: dict): + expected_features = set(features) - set(DEFAULT_FEATURES) + actual_features = set(frame) + + error_message = validate_features_presence(actual_features, expected_features) + + common_features = actual_features & expected_features + for name in common_features - {"task"}: + error_message += validate_feature_dtype_and_shape(name, features[name], frame[name]) + + if error_message: + raise ValueError(error_message) + + +def validate_features_presence(actual_features: set[str], expected_features: set[str]): + error_message = "" + missing_features = expected_features - actual_features + extra_features = actual_features - expected_features + + if missing_features or extra_features: + error_message += "Feature mismatch in `frame` dictionary:\n" + if missing_features: + error_message += f"Missing features: {missing_features}\n" + if extra_features: + error_message += f"Extra features: {extra_features}\n" + + return error_message + + +def validate_feature_dtype_and_shape(name: str, feature: dict, value: np.ndarray | PILImage.Image | str): + expected_dtype = feature["dtype"] + expected_shape = feature["shape"] + if is_valid_numpy_dtype_string(expected_dtype): + return validate_feature_numpy_array(name, expected_dtype, expected_shape, value) + elif expected_dtype in ["image", "video"]: + return validate_feature_image_or_video(name, expected_shape, value) + elif expected_dtype == "string": + return validate_feature_string(name, value) + else: + raise NotImplementedError(f"The feature dtype '{expected_dtype}' is not implemented yet.") + + +def validate_feature_numpy_array( + name: str, expected_dtype: str, expected_shape: list[int], value: np.ndarray +): + error_message = "" + if isinstance(value, np.ndarray): + actual_dtype = value.dtype + actual_shape = value.shape + + if actual_dtype != np.dtype(expected_dtype): + error_message += f"The feature '{name}' of dtype '{actual_dtype}' is not of the expected dtype '{expected_dtype}'.\n" + + if actual_shape != expected_shape: + error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{expected_shape}'.\n" + else: + error_message += f"The feature '{name}' is not a 'np.ndarray'. Expected type is '{expected_dtype}', but type '{type(value)}' provided instead.\n" + + return error_message + + +def validate_feature_image_or_video(name: str, expected_shape: list[str], value: np.ndarray | PILImage.Image): + # Note: The check of pixels range ([0,1] for float and [0,255] for uint8) is done by the image writer threads. + error_message = "" + if isinstance(value, np.ndarray): + actual_shape = value.shape + c, h, w = expected_shape + if len(actual_shape) != 3 or (actual_shape != (c, h, w) and actual_shape != (h, w, c)): + error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{(c, h, w)}' or '{(h, w, c)}'.\n" + elif isinstance(value, PILImage.Image): + pass + else: + error_message += f"The feature '{name}' is expected to be of type 'PIL.Image' or 'np.ndarray' channel first or channel last, but type '{type(value)}' provided instead.\n" + + return error_message + + +def validate_feature_string(name: str, value: str): + if not isinstance(value, str): + return f"The feature '{name}' is expected to be of type 'str', but type '{type(value)}' provided instead.\n" + return "" + + +def validate_episode_buffer(episode_buffer: dict, total_episodes: int, features: dict): + if "size" not in episode_buffer: + raise ValueError("size key not found in episode_buffer") + + if "task" not in episode_buffer: + raise ValueError("task key not found in episode_buffer") + + if episode_buffer["episode_index"] != total_episodes: + # TODO(aliberts): Add option to use existing episode_index + raise NotImplementedError( + "You might have manually provided the episode_buffer with an episode_index that doesn't " + "match the total number of episodes already in the dataset. This is not supported for now." + ) + + if episode_buffer["size"] == 0: + raise ValueError("You must add one or several frames with `add_frame` before calling `add_episode`.") + + buffer_keys = set(episode_buffer.keys()) - {"task", "size"} + if not buffer_keys == set(features): + raise ValueError( + f"Features from `episode_buffer` don't match the ones in `features`." + f"In episode_buffer not in features: {buffer_keys - set(features)}" + f"In features not in episode_buffer: {set(features) - buffer_keys}" + ) diff --git a/lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py b/lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..578353e4645e649d95df2b089e57b1439bf61e21 --- /dev/null +++ b/lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py @@ -0,0 +1,884 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script is for internal use to convert all datasets under the 'lerobot' hub user account to v2. + +Note: Since the original Aloha datasets don't use shadow motors, you need to comment those out in +lerobot/configs/robot/aloha.yaml before running this script. +""" + +import traceback +from pathlib import Path +from textwrap import dedent + +from lerobot import available_datasets +from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset +from lerobot.common.robots.aloha.configuration_aloha import AlohaRobotConfig + +LOCAL_DIR = Path("data/") + +# spellchecker:off +ALOHA_MOBILE_INFO = { + "robot_config": AlohaRobotConfig(), + "license": "mit", + "url": "https://mobile-aloha.github.io/", + "paper": "https://huggingface.co/papers/2401.02117", + "citation_bibtex": dedent(r""" + @inproceedings{fu2024mobile, + author = {Fu, Zipeng and Zhao, Tony Z. and Finn, Chelsea}, + title = {Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation}, + booktitle = {arXiv}, + year = {2024}, + }""").lstrip(), +} +ALOHA_STATIC_INFO = { + "robot_config": AlohaRobotConfig(), + "license": "mit", + "url": "https://tonyzhaozh.github.io/aloha/", + "paper": "https://huggingface.co/papers/2304.13705", + "citation_bibtex": dedent(r""" + @article{Zhao2023LearningFB, + title={Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware}, + author={Tony Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn}, + journal={RSS}, + year={2023}, + volume={abs/2304.13705}, + url={https://huggingface.co/papers/2304.13705} + }""").lstrip(), +} +PUSHT_INFO = { + "license": "mit", + "url": "https://diffusion-policy.cs.columbia.edu/", + "paper": "https://huggingface.co/papers/2303.04137", + "citation_bibtex": dedent(r""" + @article{chi2024diffusionpolicy, + author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song}, + title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, + journal = {The International Journal of Robotics Research}, + year = {2024}, + }""").lstrip(), +} +XARM_INFO = { + "license": "mit", + "url": "https://www.nicklashansen.com/td-mpc/", + "paper": "https://huggingface.co/papers/2203.04955", + "citation_bibtex": dedent(r""" + @inproceedings{Hansen2022tdmpc, + title={Temporal Difference Learning for Model Predictive Control}, + author={Nicklas Hansen and Xiaolong Wang and Hao Su}, + booktitle={ICML}, + year={2022} + } + """), +} +UNITREEH_INFO = { + "license": "apache-2.0", +} + +DATASETS = { + "aloha_mobile_cabinet": { + "single_task": "Open the top cabinet, store the pot inside it then close the cabinet.", + **ALOHA_MOBILE_INFO, + }, + "aloha_mobile_chair": { + "single_task": "Push the chairs in front of the desk to place them against it.", + **ALOHA_MOBILE_INFO, + }, + "aloha_mobile_elevator": { + "single_task": "Take the elevator to the 1st floor.", + **ALOHA_MOBILE_INFO, + }, + "aloha_mobile_shrimp": { + "single_task": "Sauté the raw shrimp on both sides, then serve it in the bowl.", + **ALOHA_MOBILE_INFO, + }, + "aloha_mobile_wash_pan": { + "single_task": "Pick up the pan, rinse it in the sink and then place it in the drying rack.", + **ALOHA_MOBILE_INFO, + }, + "aloha_mobile_wipe_wine": { + "single_task": "Pick up the wet cloth on the faucet and use it to clean the spilled wine on the table and underneath the glass.", + **ALOHA_MOBILE_INFO, + }, + "aloha_static_battery": { + "single_task": "Place the battery into the slot of the remote controller.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_candy": {"single_task": "Pick up the candy and unwrap it.", **ALOHA_STATIC_INFO}, + "aloha_static_coffee": { + "single_task": "Place the coffee capsule inside the capsule container, then place the cup onto the center of the cup tray, then push the 'Hot Water' and 'Travel Mug' buttons.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_coffee_new": { + "single_task": "Place the coffee capsule inside the capsule container, then place the cup onto the center of the cup tray.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_cups_open": { + "single_task": "Pick up the plastic cup and open its lid.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_fork_pick_up": { + "single_task": "Pick up the fork and place it on the plate.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_pingpong_test": { + "single_task": "Transfer one of the two balls in the right glass into the left glass, then transfer it back to the right glass.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_pro_pencil": { + "single_task": "Pick up the pencil with the right arm, hand it over to the left arm then place it back onto the table.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_screw_driver": { + "single_task": "Pick up the screwdriver with the right arm, hand it over to the left arm then place it into the cup.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_tape": { + "single_task": "Cut a small piece of tape from the tape dispenser then place it on the cardboard box's edge.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_thread_velcro": { + "single_task": "Pick up the velcro cable tie with the left arm, then insert the end of the velcro tie into the other end's loop with the right arm.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_towel": { + "single_task": "Pick up a piece of paper towel and place it on the spilled liquid.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_vinh_cup": { + "single_task": "Pick up the plastic cup with the right arm, then pop its lid open with the left arm.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_vinh_cup_left": { + "single_task": "Pick up the plastic cup with the left arm, then pop its lid open with the right arm.", + **ALOHA_STATIC_INFO, + }, + "aloha_static_ziploc_slide": {"single_task": "Slide open the ziploc bag.", **ALOHA_STATIC_INFO}, + "aloha_sim_insertion_scripted": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO}, + "aloha_sim_insertion_scripted_image": { + "single_task": "Insert the peg into the socket.", + **ALOHA_STATIC_INFO, + }, + "aloha_sim_insertion_human": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO}, + "aloha_sim_insertion_human_image": { + "single_task": "Insert the peg into the socket.", + **ALOHA_STATIC_INFO, + }, + "aloha_sim_transfer_cube_scripted": { + "single_task": "Pick up the cube with the right arm and transfer it to the left arm.", + **ALOHA_STATIC_INFO, + }, + "aloha_sim_transfer_cube_scripted_image": { + "single_task": "Pick up the cube with the right arm and transfer it to the left arm.", + **ALOHA_STATIC_INFO, + }, + "aloha_sim_transfer_cube_human": { + "single_task": "Pick up the cube with the right arm and transfer it to the left arm.", + **ALOHA_STATIC_INFO, + }, + "aloha_sim_transfer_cube_human_image": { + "single_task": "Pick up the cube with the right arm and transfer it to the left arm.", + **ALOHA_STATIC_INFO, + }, + "pusht": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO}, + "pusht_image": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO}, + "unitreeh1_fold_clothes": {"single_task": "Fold the sweatshirt.", **UNITREEH_INFO}, + "unitreeh1_rearrange_objects": {"single_task": "Put the object into the bin.", **UNITREEH_INFO}, + "unitreeh1_two_robot_greeting": { + "single_task": "Greet the other robot with a high five.", + **UNITREEH_INFO, + }, + "unitreeh1_warehouse": { + "single_task": "Grab the spray paint on the shelf and place it in the bin on top of the robot dog.", + **UNITREEH_INFO, + }, + "xarm_lift_medium": {"single_task": "Pick up the cube and lift it.", **XARM_INFO}, + "xarm_lift_medium_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO}, + "xarm_lift_medium_replay": {"single_task": "Pick up the cube and lift it.", **XARM_INFO}, + "xarm_lift_medium_replay_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO}, + "xarm_push_medium": {"single_task": "Push the cube onto the target.", **XARM_INFO}, + "xarm_push_medium_image": {"single_task": "Push the cube onto the target.", **XARM_INFO}, + "xarm_push_medium_replay": {"single_task": "Push the cube onto the target.", **XARM_INFO}, + "xarm_push_medium_replay_image": {"single_task": "Push the cube onto the target.", **XARM_INFO}, + "umi_cup_in_the_wild": { + "single_task": "Put the cup on the plate.", + "license": "apache-2.0", + }, + "asu_table_top": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://link.springer.com/article/10.1007/s10514-023-10129-1", + "citation_bibtex": dedent(r""" + @inproceedings{zhou2023modularity, + title={Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation}, + author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Stepputtis, Simon and Amor, Heni}, + booktitle={Conference on Robot Learning}, + pages={1684--1695}, + year={2023}, + organization={PMLR} + } + @article{zhou2023learning, + title={Learning modular language-conditioned robot policies through attention}, + author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Ben Amor, Heni and Stepputtis, Simon}, + journal={Autonomous Robots}, + pages={1--21}, + year={2023}, + publisher={Springer} + }""").lstrip(), + }, + "austin_buds_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://ut-austin-rpl.github.io/BUDS-website/", + "paper": "https://huggingface.co/papers/2109.13841", + "citation_bibtex": dedent(r""" + @article{zhu2022bottom, + title={Bottom-Up Skill Discovery From Unsegmented Demonstrations for Long-Horizon Robot Manipulation}, + author={Zhu, Yifeng and Stone, Peter and Zhu, Yuke}, + journal={IEEE Robotics and Automation Letters}, + volume={7}, + number={2}, + pages={4126--4133}, + year={2022}, + publisher={IEEE} + }""").lstrip(), + }, + "austin_sailor_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://ut-austin-rpl.github.io/sailor/", + "paper": "https://huggingface.co/papers/2210.11435", + "citation_bibtex": dedent(r""" + @inproceedings{nasiriany2022sailor, + title={Learning and Retrieval from Prior Data for Skill-based Imitation Learning}, + author={Soroush Nasiriany and Tian Gao and Ajay Mandlekar and Yuke Zhu}, + booktitle={Conference on Robot Learning (CoRL)}, + year={2022} + }""").lstrip(), + }, + "austin_sirius_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://ut-austin-rpl.github.io/sirius/", + "paper": "https://huggingface.co/papers/2211.08416", + "citation_bibtex": dedent(r""" + @inproceedings{liu2022robot, + title = {Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment}, + author = {Huihan Liu and Soroush Nasiriany and Lance Zhang and Zhiyao Bao and Yuke Zhu}, + booktitle = {Robotics: Science and Systems (RSS)}, + year = {2023} + }""").lstrip(), + }, + "berkeley_autolab_ur5": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "url": "https://sites.google.com/view/berkeley-ur5/home", + "citation_bibtex": dedent(r""" + @misc{BerkeleyUR5Website, + title = {Berkeley {UR5} Demonstration Dataset}, + author = {Lawrence Yunliang Chen and Simeon Adebola and Ken Goldberg}, + howpublished = {https://sites.google.com/view/berkeley-ur5/home}, + }""").lstrip(), + }, + "berkeley_cable_routing": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "url": "https://sites.google.com/view/cablerouting/home", + "paper": "https://huggingface.co/papers/2307.08927", + "citation_bibtex": dedent(r""" + @article{luo2023multistage, + author = {Jianlan Luo and Charles Xu and Xinyang Geng and Gilbert Feng and Kuan Fang and Liam Tan and Stefan Schaal and Sergey Levine}, + title = {Multi-Stage Cable Routing through Hierarchical Imitation Learning}, + journal = {arXiv pre-print}, + year = {2023}, + url = {https://huggingface.co/papers/2307.08927}, + }""").lstrip(), + }, + "berkeley_fanuc_manipulation": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://sites.google.com/berkeley.edu/fanuc-manipulation", + "citation_bibtex": dedent(r""" + @article{fanuc_manipulation2023, + title={Fanuc Manipulation: A Dataset for Learning-based Manipulation with FANUC Mate 200iD Robot}, + author={Zhu, Xinghao and Tian, Ran and Xu, Chenfeng and Ding, Mingyu and Zhan, Wei and Tomizuka, Masayoshi}, + year={2023}, + }""").lstrip(), + }, + "berkeley_gnm_cory_hall": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://huggingface.co/papers/1709.10489", + "citation_bibtex": dedent(r""" + @inproceedings{kahn2018self, + title={Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation}, + author={Kahn, Gregory and Villaflor, Adam and Ding, Bosen and Abbeel, Pieter and Levine, Sergey}, + booktitle={2018 IEEE international conference on robotics and automation (ICRA)}, + pages={5129--5136}, + year={2018}, + organization={IEEE} + }""").lstrip(), + }, + "berkeley_gnm_recon": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://sites.google.com/view/recon-robot", + "paper": "https://huggingface.co/papers/2104.05859", + "citation_bibtex": dedent(r""" + @inproceedings{shah2021rapid, + title={Rapid Exploration for Open-World Navigation with Latent Goal Models}, + author={Dhruv Shah and Benjamin Eysenbach and Nicholas Rhinehart and Sergey Levine}, + booktitle={5th Annual Conference on Robot Learning }, + year={2021}, + url={https://openreview.net/forum?id=d_SWJhyKfVw} + }""").lstrip(), + }, + "berkeley_gnm_sac_son": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://sites.google.com/view/SACSoN-review", + "paper": "https://huggingface.co/papers/2306.01874", + "citation_bibtex": dedent(r""" + @article{hirose2023sacson, + title={SACSoN: Scalable Autonomous Data Collection for Social Navigation}, + author={Hirose, Noriaki and Shah, Dhruv and Sridhar, Ajay and Levine, Sergey}, + journal={arXiv preprint arXiv:2306.01874}, + year={2023} + }""").lstrip(), + }, + "berkeley_mvp": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://huggingface.co/papers/2203.06173", + "citation_bibtex": dedent(r""" + @InProceedings{Radosavovic2022, + title = {Real-World Robot Learning with Masked Visual Pre-training}, + author = {Ilija Radosavovic and Tete Xiao and Stephen James and Pieter Abbeel and Jitendra Malik and Trevor Darrell}, + booktitle = {CoRL}, + year = {2022} + }""").lstrip(), + }, + "berkeley_rpt": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://huggingface.co/papers/2306.10007", + "citation_bibtex": dedent(r""" + @article{Radosavovic2023, + title={Robot Learning with Sensorimotor Pre-training}, + author={Ilija Radosavovic and Baifeng Shi and Letian Fu and Ken Goldberg and Trevor Darrell and Jitendra Malik}, + year={2023}, + journal={arXiv:2306.10007} + }""").lstrip(), + }, + "cmu_franka_exploration_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://human-world-model.github.io/", + "paper": "https://huggingface.co/papers/2308.10901", + "citation_bibtex": dedent(r""" + @inproceedings{mendonca2023structured, + title={Structured World Models from Human Videos}, + author={Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak}, + journal={RSS}, + year={2023} + }""").lstrip(), + }, + "cmu_play_fusion": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://play-fusion.github.io/", + "paper": "https://huggingface.co/papers/2312.04549", + "citation_bibtex": dedent(r""" + @inproceedings{chen2023playfusion, + title={PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play}, + author={Chen, Lili and Bahl, Shikhar and Pathak, Deepak}, + booktitle={CoRL}, + year={2023} + }""").lstrip(), + }, + "cmu_stretch": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://robo-affordances.github.io/", + "paper": "https://huggingface.co/papers/2304.08488", + "citation_bibtex": dedent(r""" + @inproceedings{bahl2023affordances, + title={Affordances from Human Videos as a Versatile Representation for Robotics}, + author={Bahl, Shikhar and Mendonca, Russell and Chen, Lili and Jain, Unnat and Pathak, Deepak}, + booktitle={CVPR}, + year={2023} + } + @article{mendonca2023structured, + title={Structured World Models from Human Videos}, + author={Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak}, + journal={CoRL}, + year={2023} + }""").lstrip(), + }, + "columbia_cairlab_pusht_real": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://diffusion-policy.cs.columbia.edu/", + "paper": "https://huggingface.co/papers/2303.04137", + "citation_bibtex": dedent(r""" + @inproceedings{chi2023diffusionpolicy, + title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, + author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran}, + booktitle={Proceedings of Robotics: Science and Systems (RSS)}, + year={2023} + }""").lstrip(), + }, + "conq_hose_manipulation": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://sites.google.com/view/conq-hose-manipulation-dataset/home", + "citation_bibtex": dedent(r""" + @misc{ConqHoseManipData, + author={Peter Mitrano and Dmitry Berenson}, + title={Conq Hose Manipulation Dataset, v1.15.0}, + year={2024}, + howpublished={https://sites.google.com/view/conq-hose-manipulation-dataset} + }""").lstrip(), + }, + "dlr_edan_shared_control": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://ieeexplore.ieee.org/document/9341156", + "citation_bibtex": dedent(r""" + @inproceedings{vogel_edan_2020, + title = {EDAN - an EMG-Controlled Daily Assistant to Help People with Physical Disabilities}, + language = {en}, + booktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})}, + author = {Vogel, Jörn and Hagengruber, Annette and Iskandar, Maged and Quere, Gabriel and Leipscher, Ulrike and Bustamante, Samuel and Dietrich, Alexander and Hoeppner, Hannes and Leidner, Daniel and Albu-Schäffer, Alin}, + year = {2020} + } + @inproceedings{quere_shared_2020, + address = {Paris, France}, + title = {Shared {Control} {Templates} for {Assistive} {Robotics}}, + language = {en}, + booktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})}, + author = {Quere, Gabriel and Hagengruber, Annette and Iskandar, Maged and Bustamante, Samuel and Leidner, Daniel and Stulp, Freek and Vogel, Joern}, + year = {2020}, + pages = {7}, + }""").lstrip(), + }, + "dlr_sara_grid_clamp": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://www.researchsquare.com/article/rs-3289569/v1", + "citation_bibtex": dedent(r""" + @article{padalkar2023guided, + title={A guided reinforcement learning approach using shared control templates for learning manipulation skills in the real world}, + author={Padalkar, Abhishek and Quere, Gabriel and Raffin, Antonin and Silv{\'e}rio, Jo{\~a}o and Stulp, Freek}, + journal={Research square preprint rs-3289569/v1}, + year={2023} + }""").lstrip(), + }, + "dlr_sara_pour": { + "tasks_col": "language_instruction", + "license": "mit", + "paper": "https://elib.dlr.de/193739/1/padalkar2023rlsct.pdf", + "citation_bibtex": dedent(r""" + @inproceedings{padalkar2023guiding, + title={Guiding Reinforcement Learning with Shared Control Templates}, + author={Padalkar, Abhishek and Quere, Gabriel and Steinmetz, Franz and Raffin, Antonin and Nieuwenhuisen, Matthias and Silv{\'e}rio, Jo{\~a}o and Stulp, Freek}, + booktitle={40th IEEE International Conference on Robotics and Automation, ICRA 2023}, + year={2023}, + organization={IEEE} + }""").lstrip(), + }, + "droid_100": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://droid-dataset.github.io/", + "paper": "https://huggingface.co/papers/2403.12945", + "citation_bibtex": dedent(r""" + @article{khazatsky2024droid, + title = {DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset}, + author = {Alexander Khazatsky and Karl Pertsch and Suraj Nair and Ashwin Balakrishna and Sudeep Dasari and Siddharth Karamcheti and Soroush Nasiriany and Mohan Kumar Srirama and Lawrence Yunliang Chen and Kirsty Ellis and Peter David Fagan and Joey Hejna and Masha Itkina and Marion Lepert and Yecheng Jason Ma and Patrick Tree Miller and Jimmy Wu and Suneel Belkhale and Shivin Dass and Huy Ha and Arhan Jain and Abraham Lee and Youngwoon Lee and Marius Memmel and Sungjae Park and Ilija Radosavovic and Kaiyuan Wang and Albert Zhan and Kevin Black and Cheng Chi and Kyle Beltran Hatch and Shan Lin and Jingpei Lu and Jean Mercat and Abdul Rehman and Pannag R Sanketi and Archit Sharma and Cody Simpson and Quan Vuong and Homer Rich Walke and Blake Wulfe and Ted Xiao and Jonathan Heewon Yang and Arefeh Yavary and Tony Z. Zhao and Christopher Agia and Rohan Baijal and Mateo Guaman Castro and Daphne Chen and Qiuyu Chen and Trinity Chung and Jaimyn Drake and Ethan Paul Foster and Jensen Gao and David Antonio Herrera and Minho Heo and Kyle Hsu and Jiaheng Hu and Donovon Jackson and Charlotte Le and Yunshuang Li and Kevin Lin and Roy Lin and Zehan Ma and Abhiram Maddukuri and Suvir Mirchandani and Daniel Morton and Tony Nguyen and Abigail O'Neill and Rosario Scalise and Derick Seale and Victor Son and Stephen Tian and Emi Tran and Andrew E. Wang and Yilin Wu and Annie Xie and Jingyun Yang and Patrick Yin and Yunchu Zhang and Osbert Bastani and Glen Berseth and Jeannette Bohg and Ken Goldberg and Abhinav Gupta and Abhishek Gupta and Dinesh Jayaraman and Joseph J Lim and Jitendra Malik and Roberto Martín-Martín and Subramanian Ramamoorthy and Dorsa Sadigh and Shuran Song and Jiajun Wu and Michael C. Yip and Yuke Zhu and Thomas Kollar and Sergey Levine and Chelsea Finn}, + year = {2024}, + }""").lstrip(), + }, + "fmb": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "url": "https://functional-manipulation-benchmark.github.io/", + "paper": "https://huggingface.co/papers/2401.08553", + "citation_bibtex": dedent(r""" + @article{luo2024fmb, + title={FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning}, + author={Luo, Jianlan and Xu, Charles and Liu, Fangchen and Tan, Liam and Lin, Zipeng and Wu, Jeffrey and Abbeel, Pieter and Levine, Sergey}, + journal={arXiv preprint arXiv:2401.08553}, + year={2024} + }""").lstrip(), + }, + "iamlab_cmu_pickup_insert": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://openreview.net/forum?id=WuBv9-IGDUA", + "paper": "https://huggingface.co/papers/2401.14502", + "citation_bibtex": dedent(r""" + @inproceedings{saxena2023multiresolution, + title={Multi-Resolution Sensing for Real-Time Control with Vision-Language Models}, + author={Saumya Saxena and Mohit Sharma and Oliver Kroemer}, + booktitle={7th Annual Conference on Robot Learning}, + year={2023}, + url={https://openreview.net/forum?id=WuBv9-IGDUA} + }""").lstrip(), + }, + "imperialcollege_sawyer_wrist_cam": { + "tasks_col": "language_instruction", + "license": "mit", + }, + "jaco_play": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "url": "https://github.com/clvrai/clvr_jaco_play_dataset", + "citation_bibtex": dedent(r""" + @software{dass2023jacoplay, + author = {Dass, Shivin and Yapeter, Jullian and Zhang, Jesse and Zhang, Jiahui + and Pertsch, Karl and Nikolaidis, Stefanos and Lim, Joseph J.}, + title = {CLVR Jaco Play Dataset}, + url = {https://github.com/clvrai/clvr_jaco_play_dataset}, + version = {1.0.0}, + year = {2023} + }""").lstrip(), + }, + "kaist_nonprehensile": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "url": "https://github.com/JaeHyung-Kim/rlds_dataset_builder", + "citation_bibtex": dedent(r""" + @article{kimpre, + title={Pre-and post-contact policy decomposition for non-prehensile manipulation with zero-shot sim-to-real transfer}, + author={Kim, Minchan and Han, Junhyek and Kim, Jaehyung and Kim, Beomjoon}, + booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + year={2023}, + organization={IEEE} + }""").lstrip(), + }, + "nyu_door_opening_surprising_effectiveness": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://jyopari.github.io/VINN/", + "paper": "https://huggingface.co/papers/2112.01511", + "citation_bibtex": dedent(r""" + @misc{pari2021surprising, + title={The Surprising Effectiveness of Representation Learning for Visual Imitation}, + author={Jyothish Pari and Nur Muhammad Shafiullah and Sridhar Pandian Arunachalam and Lerrel Pinto}, + year={2021}, + eprint={2112.01511}, + archivePrefix={arXiv}, + primaryClass={cs.RO} + }""").lstrip(), + }, + "nyu_franka_play_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://play-to-policy.github.io/", + "paper": "https://huggingface.co/papers/2210.10047", + "citation_bibtex": dedent(r""" + @article{cui2022play, + title = {From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data}, + author = {Cui, Zichen Jeff and Wang, Yibin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel}, + journal = {arXiv preprint arXiv:2210.10047}, + year = {2022} + }""").lstrip(), + }, + "nyu_rot_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://rot-robot.github.io/", + "paper": "https://huggingface.co/papers/2206.15469", + "citation_bibtex": dedent(r""" + @inproceedings{haldar2023watch, + title={Watch and match: Supercharging imitation with regularized optimal transport}, + author={Haldar, Siddhant and Mathur, Vaibhav and Yarats, Denis and Pinto, Lerrel}, + booktitle={Conference on Robot Learning}, + pages={32--43}, + year={2023}, + organization={PMLR} + }""").lstrip(), + }, + "roboturk": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://roboturk.stanford.edu/dataset_real.html", + "paper": "PAPER", + "citation_bibtex": dedent(r""" + @inproceedings{mandlekar2019scaling, + title={Scaling robot supervision to hundreds of hours with roboturk: Robotic manipulation dataset through human reasoning and dexterity}, + author={Mandlekar, Ajay and Booher, Jonathan and Spero, Max and Tung, Albert and Gupta, Anchit and Zhu, Yuke and Garg, Animesh and Savarese, Silvio and Fei-Fei, Li}, + booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, + pages={1048--1055}, + year={2019}, + organization={IEEE} + }""").lstrip(), + }, + "stanford_hydra_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://sites.google.com/view/hydra-il-2023", + "paper": "https://huggingface.co/papers/2306.17237", + "citation_bibtex": dedent(r""" + @article{belkhale2023hydra, + title={HYDRA: Hybrid Robot Actions for Imitation Learning}, + author={Belkhale, Suneel and Cui, Yuchen and Sadigh, Dorsa}, + journal={arxiv}, + year={2023} + }""").lstrip(), + }, + "stanford_kuka_multimodal_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://sites.google.com/view/visionandtouch", + "paper": "https://huggingface.co/papers/1810.10191", + "citation_bibtex": dedent(r""" + @inproceedings{lee2019icra, + title={Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks}, + author={Lee, Michelle A and Zhu, Yuke and Srinivasan, Krishnan and Shah, Parth and Savarese, Silvio and Fei-Fei, Li and Garg, Animesh and Bohg, Jeannette}, + booktitle={2019 IEEE International Conference on Robotics and Automation (ICRA)}, + year={2019}, + url={https://huggingface.co/papers/1810.10191} + }""").lstrip(), + }, + "stanford_robocook": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://hshi74.github.io/robocook/", + "paper": "https://huggingface.co/papers/2306.14447", + "citation_bibtex": dedent(r""" + @article{shi2023robocook, + title={RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools}, + author={Shi, Haochen and Xu, Huazhe and Clarke, Samuel and Li, Yunzhu and Wu, Jiajun}, + journal={arXiv preprint arXiv:2306.14447}, + year={2023} + }""").lstrip(), + }, + "taco_play": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "url": "https://www.kaggle.com/datasets/oiermees/taco-robot", + "paper": "https://huggingface.co/papers/2209.08959, https://huggingface.co/papers/2210.01911", + "citation_bibtex": dedent(r""" + @inproceedings{rosete2022tacorl, + author = {Erick Rosete-Beas and Oier Mees and Gabriel Kalweit and Joschka Boedecker and Wolfram Burgard}, + title = {Latent Plans for Task Agnostic Offline Reinforcement Learning}, + journal = {Proceedings of the 6th Conference on Robot Learning (CoRL)}, + year = {2022} + } + @inproceedings{mees23hulc2, + title={Grounding Language with Visual Affordances over Unstructured Data}, + author={Oier Mees and Jessica Borja-Diaz and Wolfram Burgard}, + booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)}, + year={2023}, + address = {London, UK} + }""").lstrip(), + }, + "tokyo_u_lsmo": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "URL", + "paper": "https://huggingface.co/papers/2107.05842", + "citation_bibtex": dedent(r""" + @Article{Osa22, + author = {Takayuki Osa}, + journal = {The International Journal of Robotics Research}, + title = {Motion Planning by Learning the Solution Manifold in Trajectory Optimization}, + year = {2022}, + number = {3}, + pages = {291--311}, + volume = {41}, + }""").lstrip(), + }, + "toto": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://toto-benchmark.org/", + "paper": "https://huggingface.co/papers/2306.00942", + "citation_bibtex": dedent(r""" + @inproceedings{zhou2023train, + author={Zhou, Gaoyue and Dean, Victoria and Srirama, Mohan Kumar and Rajeswaran, Aravind and Pari, Jyothish and Hatch, Kyle and Jain, Aryan and Yu, Tianhe and Abbeel, Pieter and Pinto, Lerrel and Finn, Chelsea and Gupta, Abhinav}, + booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, + title={Train Offline, Test Online: A Real Robot Learning Benchmark}, + year={2023}, + }""").lstrip(), + }, + "ucsd_kitchen_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "citation_bibtex": dedent(r""" + @ARTICLE{ucsd_kitchens, + author = {Ge Yan, Kris Wu, and Xiaolong Wang}, + title = {{ucsd kitchens Dataset}}, + year = {2023}, + month = {August} + }""").lstrip(), + }, + "ucsd_pick_and_place_dataset": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://owmcorl.github.io/#", + "paper": "https://huggingface.co/papers/2310.16029", + "citation_bibtex": dedent(r""" + @preprint{Feng2023Finetuning, + title={Finetuning Offline World Models in the Real World}, + author={Yunhai Feng, Nicklas Hansen, Ziyan Xiong, Chandramouli Rajagopalan, Xiaolong Wang}, + year={2023} + }""").lstrip(), + }, + "uiuc_d3field": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://robopil.github.io/d3fields/", + "paper": "https://huggingface.co/papers/2309.16118", + "citation_bibtex": dedent(r""" + @article{wang2023d3field, + title={D^3Field: Dynamic 3D Descriptor Fields for Generalizable Robotic Manipulation}, + author={Wang, Yixuan and Li, Zhuoran and Zhang, Mingtong and Driggs-Campbell, Katherine and Wu, Jiajun and Fei-Fei, Li and Li, Yunzhu}, + journal={arXiv preprint arXiv:}, + year={2023}, + }""").lstrip(), + }, + "usc_cloth_sim": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://uscresl.github.io/dmfd/", + "paper": "https://huggingface.co/papers/2207.10148", + "citation_bibtex": dedent(r""" + @article{salhotra2022dmfd, + author={Salhotra, Gautam and Liu, I-Chun Arthur and Dominguez-Kuhne, Marcus and Sukhatme, Gaurav S.}, + journal={IEEE Robotics and Automation Letters}, + title={Learning Deformable Object Manipulation From Expert Demonstrations}, + year={2022}, + volume={7}, + number={4}, + pages={8775-8782}, + doi={10.1109/LRA.2022.3187843} + }""").lstrip(), + }, + "utaustin_mutex": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://ut-austin-rpl.github.io/MUTEX/", + "paper": "https://huggingface.co/papers/2309.14320", + "citation_bibtex": dedent(r""" + @inproceedings{shah2023mutex, + title={{MUTEX}: Learning Unified Policies from Multimodal Task Specifications}, + author={Rutav Shah and Roberto Mart{\'\i}n-Mart{\'\i}n and Yuke Zhu}, + booktitle={7th Annual Conference on Robot Learning}, + year={2023}, + url={https://openreview.net/forum?id=PwqiqaaEzJ} + }""").lstrip(), + }, + "utokyo_pr2_opening_fridge": { + "tasks_col": "language_instruction", + "license": "mit", + "citation_bibtex": dedent(r""" + @misc{oh2023pr2utokyodatasets, + author={Jihoon Oh and Naoaki Kanazawa and Kento Kawaharazuka}, + title={X-Embodiment U-Tokyo PR2 Datasets}, + year={2023}, + url={https://github.com/ojh6404/rlds_dataset_builder}, + }""").lstrip(), + }, + "utokyo_pr2_tabletop_manipulation": { + "tasks_col": "language_instruction", + "license": "mit", + "citation_bibtex": dedent(r""" + @misc{oh2023pr2utokyodatasets, + author={Jihoon Oh and Naoaki Kanazawa and Kento Kawaharazuka}, + title={X-Embodiment U-Tokyo PR2 Datasets}, + year={2023}, + url={https://github.com/ojh6404/rlds_dataset_builder}, + }""").lstrip(), + }, + "utokyo_saytap": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://saytap.github.io/", + "paper": "https://huggingface.co/papers/2306.07580", + "citation_bibtex": dedent(r""" + @article{saytap2023, + author = {Yujin Tang and Wenhao Yu and Jie Tan and Heiga Zen and Aleksandra Faust and + Tatsuya Harada}, + title = {SayTap: Language to Quadrupedal Locomotion}, + eprint = {arXiv:2306.07580}, + url = {https://saytap.github.io}, + note = {https://saytap.github.io}, + year = {2023} + }""").lstrip(), + }, + "utokyo_xarm_bimanual": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "citation_bibtex": dedent(r""" + @misc{matsushima2023weblab, + title={Weblab xArm Dataset}, + author={Tatsuya Matsushima and Hiroki Furuta and Yusuke Iwasawa and Yutaka Matsuo}, + year={2023}, + }""").lstrip(), + }, + "utokyo_xarm_pick_and_place": { + "tasks_col": "language_instruction", + "license": "cc-by-4.0", + "citation_bibtex": dedent(r""" + @misc{matsushima2023weblab, + title={Weblab xArm Dataset}, + author={Tatsuya Matsushima and Hiroki Furuta and Yusuke Iwasawa and Yutaka Matsuo}, + year={2023}, + }""").lstrip(), + }, + "viola": { + "tasks_col": "language_instruction", + "license": "mit", + "url": "https://ut-austin-rpl.github.io/VIOLA/", + "paper": "https://huggingface.co/papers/2210.11339", + "citation_bibtex": dedent(r""" + @article{zhu2022viola, + title={VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors}, + author={Zhu, Yifeng and Joshi, Abhishek and Stone, Peter and Zhu, Yuke}, + journal={6th Annual Conference on Robot Learning (CoRL)}, + year={2022} + }""").lstrip(), + }, +} +# spellchecker:on + + +def batch_convert(): + status = {} + logfile = LOCAL_DIR / "conversion_log.txt" + assert set(DATASETS) == {id_.split("/")[1] for id_ in available_datasets} + for num, (name, kwargs) in enumerate(DATASETS.items()): + repo_id = f"lerobot/{name}" + print(f"\nConverting {repo_id} ({num}/{len(DATASETS)})") + print("---------------------------------------------------------") + try: + convert_dataset(repo_id, LOCAL_DIR, **kwargs) + status = f"{repo_id}: success." + with open(logfile, "a") as file: + file.write(status + "\n") + except Exception: + status = f"{repo_id}: failed\n {traceback.format_exc()}" + with open(logfile, "a") as file: + file.write(status + "\n") + continue + + +if __name__ == "__main__": + batch_convert() diff --git a/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..c7e38b9ee4b67fb06d48c528d7aa2b3179de98f8 --- /dev/null +++ b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py @@ -0,0 +1,687 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script will help you convert any LeRobot dataset already pushed to the hub from codebase version 1.6 to +2.0. You will be required to provide the 'tasks', which is a short but accurate description in plain English +for each of the task performed in the dataset. This will allow to easily train models with task-conditioning. + +We support 3 different scenarios for these tasks (see instructions below): + 1. Single task dataset: all episodes of your dataset have the same single task. + 2. Single task episodes: the episodes of your dataset each contain a single task but they can differ from + one episode to the next. + 3. Multi task episodes: episodes of your dataset may each contain several different tasks. + + +Can you can also provide a robot config .yaml file (not mandatory) to this script via the option +'--robot-config' so that it writes information about the robot (robot type, motors names) this dataset was +recorded with. For now, only Aloha/Koch type robots are supported with this option. + + +# 1. Single task dataset +If your dataset contains a single task, you can simply provide it directly via the CLI with the +'--single-task' option. + +Examples: + +```bash +python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \ + --repo-id lerobot/aloha_sim_insertion_human_image \ + --single-task "Insert the peg into the socket." \ + --robot-config lerobot/configs/robot/aloha.yaml \ + --local-dir data +``` + +```bash +python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \ + --repo-id aliberts/koch_tutorial \ + --single-task "Pick the Lego block and drop it in the box on the right." \ + --robot-config lerobot/configs/robot/koch.yaml \ + --local-dir data +``` + + +# 2. Single task episodes +If your dataset is a multi-task dataset, you have two options to provide the tasks to this script: + +- If your dataset already contains a language instruction column in its parquet file, you can simply provide + this column's name with the '--tasks-col' arg. + + Example: + + ```bash + python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \ + --repo-id lerobot/stanford_kuka_multimodal_dataset \ + --tasks-col "language_instruction" \ + --local-dir data + ``` + +- If your dataset doesn't contain a language instruction, you should provide the path to a .json file with the + '--tasks-path' arg. This file should have the following structure where keys correspond to each + episode_index in the dataset, and values are the language instruction for that episode. + + Example: + + ```json + { + "0": "Do something", + "1": "Do something else", + "2": "Do something", + "3": "Go there", + ... + } + ``` + +# 3. Multi task episodes +If you have multiple tasks per episodes, your dataset should contain a language instruction column in its +parquet file, and you must provide this column's name with the '--tasks-col' arg. + +Example: + +```bash +python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \ + --repo-id lerobot/stanford_kuka_multimodal_dataset \ + --tasks-col "language_instruction" \ + --local-dir data +``` +""" + +import argparse +import contextlib +import filecmp +import json +import logging +import math +import shutil +import subprocess +import tempfile +from pathlib import Path + +import datasets +import pyarrow.compute as pc +import pyarrow.parquet as pq +import torch +from datasets import Dataset +from huggingface_hub import HfApi +from huggingface_hub.errors import EntryNotFoundError, HfHubHTTPError +from safetensors.torch import load_file + +from lerobot.common.datasets.utils import ( + DEFAULT_CHUNK_SIZE, + DEFAULT_PARQUET_PATH, + DEFAULT_VIDEO_PATH, + EPISODES_PATH, + INFO_PATH, + STATS_PATH, + TASKS_PATH, + create_branch, + create_lerobot_dataset_card, + flatten_dict, + get_safe_version, + load_json, + unflatten_dict, + write_json, + write_jsonlines, +) +from lerobot.common.datasets.video_utils import ( + VideoFrame, # noqa: F401 + get_image_pixel_channels, + get_video_info, +) +from lerobot.common.robots import RobotConfig + +V16 = "v1.6" +V20 = "v2.0" + +GITATTRIBUTES_REF = "aliberts/gitattributes_reference" +V1_VIDEO_FILE = "{video_key}_episode_{episode_index:06d}.mp4" +V1_INFO_PATH = "meta_data/info.json" +V1_STATS_PATH = "meta_data/stats.safetensors" + + +def parse_robot_config(robot_cfg: RobotConfig) -> tuple[str, dict]: + if robot_cfg.type in ["aloha", "koch"]: + state_names = [ + f"{arm}_{motor}" if len(robot_cfg.follower_arms) > 1 else motor + for arm in robot_cfg.follower_arms + for motor in robot_cfg.follower_arms[arm].motors + ] + action_names = [ + # f"{arm}_{motor}" for arm in ["left", "right"] for motor in robot_cfg["leader_arms"][arm]["motors"] + f"{arm}_{motor}" if len(robot_cfg.leader_arms) > 1 else motor + for arm in robot_cfg.leader_arms + for motor in robot_cfg.leader_arms[arm].motors + ] + # elif robot_cfg["robot_type"] == "stretch3": TODO + else: + raise NotImplementedError( + "Please provide robot_config={'robot_type': ..., 'names': ...} directly to convert_dataset()." + ) + + return { + "robot_type": robot_cfg.type, + "names": { + "observation.state": state_names, + "observation.effort": state_names, + "action": action_names, + }, + } + + +def convert_stats_to_json(v1_dir: Path, v2_dir: Path) -> None: + safetensor_path = v1_dir / V1_STATS_PATH + stats = load_file(safetensor_path) + serialized_stats = {key: value.tolist() for key, value in stats.items()} + serialized_stats = unflatten_dict(serialized_stats) + + json_path = v2_dir / STATS_PATH + json_path.parent.mkdir(exist_ok=True, parents=True) + with open(json_path, "w") as f: + json.dump(serialized_stats, f, indent=4) + + # Sanity check + with open(json_path) as f: + stats_json = json.load(f) + + stats_json = flatten_dict(stats_json) + stats_json = {key: torch.tensor(value) for key, value in stats_json.items()} + for key in stats: + torch.testing.assert_close(stats_json[key], stats[key]) + + +def get_features_from_hf_dataset( + dataset: Dataset, robot_config: RobotConfig | None = None +) -> dict[str, list]: + robot_config = parse_robot_config(robot_config) + features = {} + for key, ft in dataset.features.items(): + if isinstance(ft, datasets.Value): + dtype = ft.dtype + shape = (1,) + names = None + if isinstance(ft, datasets.Sequence): + assert isinstance(ft.feature, datasets.Value) + dtype = ft.feature.dtype + shape = (ft.length,) + motor_names = ( + robot_config["names"][key] if robot_config else [f"motor_{i}" for i in range(ft.length)] + ) + assert len(motor_names) == shape[0] + names = {"motors": motor_names} + elif isinstance(ft, datasets.Image): + dtype = "image" + image = dataset[0][key] # Assuming first row + channels = get_image_pixel_channels(image) + shape = (image.height, image.width, channels) + names = ["height", "width", "channels"] + elif ft._type == "VideoFrame": + dtype = "video" + shape = None # Add shape later + names = ["height", "width", "channels"] + + features[key] = { + "dtype": dtype, + "shape": shape, + "names": names, + } + + return features + + +def add_task_index_by_episodes(dataset: Dataset, tasks_by_episodes: dict) -> tuple[Dataset, list[str]]: + df = dataset.to_pandas() + tasks = list(set(tasks_by_episodes.values())) + tasks_to_task_index = {task: task_idx for task_idx, task in enumerate(tasks)} + episodes_to_task_index = {ep_idx: tasks_to_task_index[task] for ep_idx, task in tasks_by_episodes.items()} + df["task_index"] = df["episode_index"].map(episodes_to_task_index).astype(int) + + features = dataset.features + features["task_index"] = datasets.Value(dtype="int64") + dataset = Dataset.from_pandas(df, features=features, split="train") + return dataset, tasks + + +def add_task_index_from_tasks_col( + dataset: Dataset, tasks_col: str +) -> tuple[Dataset, dict[str, list[str]], list[str]]: + df = dataset.to_pandas() + + # HACK: This is to clean some of the instructions in our version of Open X datasets + prefix_to_clean = "tf.Tensor(b'" + suffix_to_clean = "', shape=(), dtype=string)" + df[tasks_col] = df[tasks_col].str.removeprefix(prefix_to_clean).str.removesuffix(suffix_to_clean) + + # Create task_index col + tasks_by_episode = df.groupby("episode_index")[tasks_col].unique().apply(lambda x: x.tolist()).to_dict() + tasks = df[tasks_col].unique().tolist() + tasks_to_task_index = {task: idx for idx, task in enumerate(tasks)} + df["task_index"] = df[tasks_col].map(tasks_to_task_index).astype(int) + + # Build the dataset back from df + features = dataset.features + features["task_index"] = datasets.Value(dtype="int64") + dataset = Dataset.from_pandas(df, features=features, split="train") + dataset = dataset.remove_columns(tasks_col) + + return dataset, tasks, tasks_by_episode + + +def split_parquet_by_episodes( + dataset: Dataset, + total_episodes: int, + total_chunks: int, + output_dir: Path, +) -> list: + table = dataset.data.table + episode_lengths = [] + for ep_chunk in range(total_chunks): + ep_chunk_start = DEFAULT_CHUNK_SIZE * ep_chunk + ep_chunk_end = min(DEFAULT_CHUNK_SIZE * (ep_chunk + 1), total_episodes) + chunk_dir = "/".join(DEFAULT_PARQUET_PATH.split("/")[:-1]).format(episode_chunk=ep_chunk) + (output_dir / chunk_dir).mkdir(parents=True, exist_ok=True) + for ep_idx in range(ep_chunk_start, ep_chunk_end): + ep_table = table.filter(pc.equal(table["episode_index"], ep_idx)) + episode_lengths.insert(ep_idx, len(ep_table)) + output_file = output_dir / DEFAULT_PARQUET_PATH.format( + episode_chunk=ep_chunk, episode_index=ep_idx + ) + pq.write_table(ep_table, output_file) + + return episode_lengths + + +def move_videos( + repo_id: str, + video_keys: list[str], + total_episodes: int, + total_chunks: int, + work_dir: Path, + clean_gittatributes: Path, + branch: str = "main", +) -> None: + """ + HACK: Since HfApi() doesn't provide a way to move files directly in a repo, this function will run git + commands to fetch git lfs video files references to move them into subdirectories without having to + actually download them. + """ + _lfs_clone(repo_id, work_dir, branch) + + videos_moved = False + video_files = [str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*.mp4")] + if len(video_files) == 0: + video_files = [str(f.relative_to(work_dir)) for f in work_dir.glob("videos*/*/*/*.mp4")] + videos_moved = True # Videos have already been moved + + assert len(video_files) == total_episodes * len(video_keys) + + lfs_untracked_videos = _get_lfs_untracked_videos(work_dir, video_files) + + current_gittatributes = work_dir / ".gitattributes" + if not filecmp.cmp(current_gittatributes, clean_gittatributes, shallow=False): + fix_gitattributes(work_dir, current_gittatributes, clean_gittatributes) + + if lfs_untracked_videos: + fix_lfs_video_files_tracking(work_dir, video_files) + + if videos_moved: + return + + video_dirs = sorted(work_dir.glob("videos*/")) + for ep_chunk in range(total_chunks): + ep_chunk_start = DEFAULT_CHUNK_SIZE * ep_chunk + ep_chunk_end = min(DEFAULT_CHUNK_SIZE * (ep_chunk + 1), total_episodes) + for vid_key in video_keys: + chunk_dir = "/".join(DEFAULT_VIDEO_PATH.split("/")[:-1]).format( + episode_chunk=ep_chunk, video_key=vid_key + ) + (work_dir / chunk_dir).mkdir(parents=True, exist_ok=True) + + for ep_idx in range(ep_chunk_start, ep_chunk_end): + target_path = DEFAULT_VIDEO_PATH.format( + episode_chunk=ep_chunk, video_key=vid_key, episode_index=ep_idx + ) + video_file = V1_VIDEO_FILE.format(video_key=vid_key, episode_index=ep_idx) + if len(video_dirs) == 1: + video_path = video_dirs[0] / video_file + else: + for dir in video_dirs: + if (dir / video_file).is_file(): + video_path = dir / video_file + break + + video_path.rename(work_dir / target_path) + + commit_message = "Move video files into chunk subdirectories" + subprocess.run(["git", "add", "."], cwd=work_dir, check=True) + subprocess.run(["git", "commit", "-m", commit_message], cwd=work_dir, check=True) + subprocess.run(["git", "push"], cwd=work_dir, check=True) + + +def fix_lfs_video_files_tracking(work_dir: Path, lfs_untracked_videos: list[str]) -> None: + """ + HACK: This function fixes the tracking by git lfs which was not properly set on some repos. In that case, + there's no other option than to download the actual files and reupload them with lfs tracking. + """ + for i in range(0, len(lfs_untracked_videos), 100): + files = lfs_untracked_videos[i : i + 100] + try: + subprocess.run(["git", "rm", "--cached", *files], cwd=work_dir, capture_output=True, check=True) + except subprocess.CalledProcessError as e: + print("git rm --cached ERROR:") + print(e.stderr) + subprocess.run(["git", "add", *files], cwd=work_dir, check=True) + + commit_message = "Track video files with git lfs" + subprocess.run(["git", "commit", "-m", commit_message], cwd=work_dir, check=True) + subprocess.run(["git", "push"], cwd=work_dir, check=True) + + +def fix_gitattributes(work_dir: Path, current_gittatributes: Path, clean_gittatributes: Path) -> None: + shutil.copyfile(clean_gittatributes, current_gittatributes) + subprocess.run(["git", "add", ".gitattributes"], cwd=work_dir, check=True) + subprocess.run(["git", "commit", "-m", "Fix .gitattributes"], cwd=work_dir, check=True) + subprocess.run(["git", "push"], cwd=work_dir, check=True) + + +def _lfs_clone(repo_id: str, work_dir: Path, branch: str) -> None: + subprocess.run(["git", "lfs", "install"], cwd=work_dir, check=True) + repo_url = f"https://huggingface.co/datasets/{repo_id}" + env = {"GIT_LFS_SKIP_SMUDGE": "1"} # Prevent downloading LFS files + subprocess.run( + ["git", "clone", "--branch", branch, "--single-branch", "--depth", "1", repo_url, str(work_dir)], + check=True, + env=env, + ) + + +def _get_lfs_untracked_videos(work_dir: Path, video_files: list[str]) -> list[str]: + lfs_tracked_files = subprocess.run( + ["git", "lfs", "ls-files", "-n"], cwd=work_dir, capture_output=True, text=True, check=True + ) + lfs_tracked_files = set(lfs_tracked_files.stdout.splitlines()) + return [f for f in video_files if f not in lfs_tracked_files] + + +def get_videos_info(repo_id: str, local_dir: Path, video_keys: list[str], branch: str) -> dict: + # Assumes first episode + video_files = [ + DEFAULT_VIDEO_PATH.format(episode_chunk=0, video_key=vid_key, episode_index=0) + for vid_key in video_keys + ] + hub_api = HfApi() + hub_api.snapshot_download( + repo_id=repo_id, repo_type="dataset", local_dir=local_dir, revision=branch, allow_patterns=video_files + ) + videos_info_dict = {} + for vid_key, vid_path in zip(video_keys, video_files, strict=True): + videos_info_dict[vid_key] = get_video_info(local_dir / vid_path) + + return videos_info_dict + + +def convert_dataset( + repo_id: str, + local_dir: Path, + single_task: str | None = None, + tasks_path: Path | None = None, + tasks_col: Path | None = None, + robot_config: RobotConfig | None = None, + test_branch: str | None = None, + **card_kwargs, +): + v1 = get_safe_version(repo_id, V16) + v1x_dir = local_dir / V16 / repo_id + v20_dir = local_dir / V20 / repo_id + v1x_dir.mkdir(parents=True, exist_ok=True) + v20_dir.mkdir(parents=True, exist_ok=True) + + hub_api = HfApi() + hub_api.snapshot_download( + repo_id=repo_id, repo_type="dataset", revision=v1, local_dir=v1x_dir, ignore_patterns="videos*/" + ) + branch = "main" + if test_branch: + branch = test_branch + create_branch(repo_id=repo_id, branch=test_branch, repo_type="dataset") + + metadata_v1 = load_json(v1x_dir / V1_INFO_PATH) + dataset = datasets.load_dataset("parquet", data_dir=v1x_dir / "data", split="train") + features = get_features_from_hf_dataset(dataset, robot_config) + video_keys = [key for key, ft in features.items() if ft["dtype"] == "video"] + + if single_task and "language_instruction" in dataset.column_names: + logging.warning( + "'single_task' provided but 'language_instruction' tasks_col found. Using 'language_instruction'.", + ) + single_task = None + tasks_col = "language_instruction" + + # Episodes & chunks + episode_indices = sorted(dataset.unique("episode_index")) + total_episodes = len(episode_indices) + assert episode_indices == list(range(total_episodes)) + total_videos = total_episodes * len(video_keys) + total_chunks = total_episodes // DEFAULT_CHUNK_SIZE + if total_episodes % DEFAULT_CHUNK_SIZE != 0: + total_chunks += 1 + + # Tasks + if single_task: + tasks_by_episodes = dict.fromkeys(episode_indices, single_task) + dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes) + tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()} + elif tasks_path: + tasks_by_episodes = load_json(tasks_path) + tasks_by_episodes = {int(ep_idx): task for ep_idx, task in tasks_by_episodes.items()} + dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes) + tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()} + elif tasks_col: + dataset, tasks, tasks_by_episodes = add_task_index_from_tasks_col(dataset, tasks_col) + else: + raise ValueError + + assert set(tasks) == {task for ep_tasks in tasks_by_episodes.values() for task in ep_tasks} + tasks = [{"task_index": task_idx, "task": task} for task_idx, task in enumerate(tasks)] + write_jsonlines(tasks, v20_dir / TASKS_PATH) + features["task_index"] = { + "dtype": "int64", + "shape": (1,), + "names": None, + } + + # Videos + if video_keys: + assert metadata_v1.get("video", False) + dataset = dataset.remove_columns(video_keys) + clean_gitattr = Path( + hub_api.hf_hub_download( + repo_id=GITATTRIBUTES_REF, repo_type="dataset", local_dir=local_dir, filename=".gitattributes" + ) + ).absolute() + with tempfile.TemporaryDirectory() as tmp_video_dir: + move_videos( + repo_id, video_keys, total_episodes, total_chunks, Path(tmp_video_dir), clean_gitattr, branch + ) + videos_info = get_videos_info(repo_id, v1x_dir, video_keys=video_keys, branch=branch) + for key in video_keys: + features[key]["shape"] = ( + videos_info[key].pop("video.height"), + videos_info[key].pop("video.width"), + videos_info[key].pop("video.channels"), + ) + features[key]["video_info"] = videos_info[key] + assert math.isclose(videos_info[key]["video.fps"], metadata_v1["fps"], rel_tol=1e-3) + if "encoding" in metadata_v1: + assert videos_info[key]["video.pix_fmt"] == metadata_v1["encoding"]["pix_fmt"] + else: + assert metadata_v1.get("video", 0) == 0 + videos_info = None + + # Split data into 1 parquet file by episode + episode_lengths = split_parquet_by_episodes(dataset, total_episodes, total_chunks, v20_dir) + + if robot_config is not None: + robot_type = robot_config.type + repo_tags = [robot_type] + else: + robot_type = "unknown" + repo_tags = None + + # Episodes + episodes = [ + {"episode_index": ep_idx, "tasks": tasks_by_episodes[ep_idx], "length": episode_lengths[ep_idx]} + for ep_idx in episode_indices + ] + write_jsonlines(episodes, v20_dir / EPISODES_PATH) + + # Assemble metadata v2.0 + metadata_v2_0 = { + "codebase_version": V20, + "robot_type": robot_type, + "total_episodes": total_episodes, + "total_frames": len(dataset), + "total_tasks": len(tasks), + "total_videos": total_videos, + "total_chunks": total_chunks, + "chunks_size": DEFAULT_CHUNK_SIZE, + "fps": metadata_v1["fps"], + "splits": {"train": f"0:{total_episodes}"}, + "data_path": DEFAULT_PARQUET_PATH, + "video_path": DEFAULT_VIDEO_PATH if video_keys else None, + "features": features, + } + write_json(metadata_v2_0, v20_dir / INFO_PATH) + convert_stats_to_json(v1x_dir, v20_dir) + card = create_lerobot_dataset_card(tags=repo_tags, dataset_info=metadata_v2_0, **card_kwargs) + + with contextlib.suppress(EntryNotFoundError, HfHubHTTPError): + hub_api.delete_folder(repo_id=repo_id, path_in_repo="data", repo_type="dataset", revision=branch) + + with contextlib.suppress(EntryNotFoundError, HfHubHTTPError): + hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta_data", repo_type="dataset", revision=branch) + + with contextlib.suppress(EntryNotFoundError, HfHubHTTPError): + hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta", repo_type="dataset", revision=branch) + + hub_api.upload_folder( + repo_id=repo_id, + path_in_repo="data", + folder_path=v20_dir / "data", + repo_type="dataset", + revision=branch, + ) + hub_api.upload_folder( + repo_id=repo_id, + path_in_repo="meta", + folder_path=v20_dir / "meta", + repo_type="dataset", + revision=branch, + ) + + card.push_to_hub(repo_id=repo_id, repo_type="dataset", revision=branch) + + if not test_branch: + create_branch(repo_id=repo_id, branch=V20, repo_type="dataset") + + +def make_robot_config(robot_type: str, **kwargs) -> RobotConfig: + if robot_type == "aloha": + raise NotImplementedError # TODO + + elif robot_type == "koch_follower": + from lerobot.common.robots.koch_follower import KochFollowerConfig + + return KochFollowerConfig(**kwargs) + elif robot_type == "so100_follower": + from lerobot.common.robots.so100_follower import SO100FollowerConfig + + return SO100FollowerConfig(**kwargs) + elif robot_type == "stretch": + from lerobot.common.robots.stretch3 import Stretch3RobotConfig + + return Stretch3RobotConfig(**kwargs) + elif robot_type == "lekiwi": + from lerobot.common.robots.lekiwi import LeKiwiConfig + + return LeKiwiConfig(**kwargs) + else: + raise ValueError(f"Robot type '{robot_type}' is not available.") + + +def main(): + parser = argparse.ArgumentParser() + task_args = parser.add_mutually_exclusive_group(required=True) + + parser.add_argument( + "--repo-id", + type=str, + required=True, + help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset (e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).", + ) + task_args.add_argument( + "--single-task", + type=str, + help="A short but accurate description of the single task performed in the dataset.", + ) + task_args.add_argument( + "--tasks-col", + type=str, + help="The name of the column containing language instructions", + ) + task_args.add_argument( + "--tasks-path", + type=Path, + help="The path to a .json file containing one language instruction for each episode_index", + ) + parser.add_argument( + "--robot", + type=str, + default=None, + help="Robot config used for the dataset during conversion (e.g. 'koch', 'aloha', 'so100', etc.)", + ) + parser.add_argument( + "--local-dir", + type=Path, + default=None, + help="Local directory to store the dataset during conversion. Defaults to /tmp/lerobot_dataset_v2", + ) + parser.add_argument( + "--license", + type=str, + default="apache-2.0", + help="Repo license. Must be one of https://huggingface.co/docs/hub/repositories-licenses. Defaults to mit.", + ) + parser.add_argument( + "--test-branch", + type=str, + default=None, + help="Repo branch to test your conversion first (e.g. 'v2.0.test')", + ) + + args = parser.parse_args() + if not args.local_dir: + args.local_dir = Path("/tmp/lerobot_dataset_v2") + + if args.robot is not None: + robot_config = make_robot_config(args.robot) + + del args.robot + + convert_dataset(**vars(args), robot_config=robot_config) + + +if __name__ == "__main__": + main() diff --git a/lerobot/common/datasets/v21/_remove_language_instruction.py b/lerobot/common/datasets/v21/_remove_language_instruction.py new file mode 100644 index 0000000000000000000000000000000000000000..e8244a3b3dcc092e3967de4fe826d0cd25dd5d6a --- /dev/null +++ b/lerobot/common/datasets/v21/_remove_language_instruction.py @@ -0,0 +1,87 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import traceback +from pathlib import Path + +from datasets import get_dataset_config_info +from huggingface_hub import HfApi + +from lerobot import available_datasets +from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata +from lerobot.common.datasets.utils import INFO_PATH, write_info +from lerobot.common.datasets.v21.convert_dataset_v20_to_v21 import V20, SuppressWarnings + +LOCAL_DIR = Path("data/") + +hub_api = HfApi() + + +def fix_dataset(repo_id: str) -> str: + if not hub_api.revision_exists(repo_id, V20, repo_type="dataset"): + return f"{repo_id}: skipped (not in {V20})." + + dataset_info = get_dataset_config_info(repo_id, "default") + with SuppressWarnings(): + lerobot_metadata = LeRobotDatasetMetadata(repo_id, revision=V20, force_cache_sync=True) + + meta_features = {key for key, ft in lerobot_metadata.features.items() if ft["dtype"] != "video"} + parquet_features = set(dataset_info.features) + + diff_parquet_meta = parquet_features - meta_features + diff_meta_parquet = meta_features - parquet_features + + if diff_parquet_meta: + raise ValueError(f"In parquet not in info.json: {parquet_features - meta_features}") + + if not diff_meta_parquet: + return f"{repo_id}: skipped (no diff)" + + if diff_meta_parquet: + logging.warning(f"In info.json not in parquet: {meta_features - parquet_features}") + assert diff_meta_parquet == {"language_instruction"} + lerobot_metadata.features.pop("language_instruction") + write_info(lerobot_metadata.info, lerobot_metadata.root) + commit_info = hub_api.upload_file( + path_or_fileobj=lerobot_metadata.root / INFO_PATH, + path_in_repo=INFO_PATH, + repo_id=repo_id, + repo_type="dataset", + revision=V20, + commit_message="Remove 'language_instruction'", + create_pr=True, + ) + return f"{repo_id}: success - PR: {commit_info.pr_url}" + + +def batch_fix(): + status = {} + LOCAL_DIR.mkdir(parents=True, exist_ok=True) + logfile = LOCAL_DIR / "fix_features_v20.txt" + for num, repo_id in enumerate(available_datasets): + print(f"\nConverting {repo_id} ({num}/{len(available_datasets)})") + print("---------------------------------------------------------") + try: + status = fix_dataset(repo_id) + except Exception: + status = f"{repo_id}: failed\n {traceback.format_exc()}" + + logging.info(status) + with open(logfile, "a") as file: + file.write(status + "\n") + + +if __name__ == "__main__": + batch_fix() diff --git a/lerobot/common/datasets/v21/batch_convert_dataset_v20_to_v21.py b/lerobot/common/datasets/v21/batch_convert_dataset_v20_to_v21.py new file mode 100644 index 0000000000000000000000000000000000000000..5116c1d069b23c8ffae59af4bb5eb8ce5f0fbcc4 --- /dev/null +++ b/lerobot/common/datasets/v21/batch_convert_dataset_v20_to_v21.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script is for internal use to convert all datasets under the 'lerobot' hub user account to v2.1. +""" + +import traceback +from pathlib import Path + +from huggingface_hub import HfApi + +from lerobot import available_datasets +from lerobot.common.datasets.v21.convert_dataset_v20_to_v21 import V21, convert_dataset + +LOCAL_DIR = Path("data/") + + +def batch_convert(): + status = {} + LOCAL_DIR.mkdir(parents=True, exist_ok=True) + logfile = LOCAL_DIR / "conversion_log_v21.txt" + hub_api = HfApi() + for num, repo_id in enumerate(available_datasets): + print(f"\nConverting {repo_id} ({num}/{len(available_datasets)})") + print("---------------------------------------------------------") + try: + if hub_api.revision_exists(repo_id, V21, repo_type="dataset"): + status = f"{repo_id}: success (already in {V21})." + else: + convert_dataset(repo_id) + status = f"{repo_id}: success." + except Exception: + status = f"{repo_id}: failed\n {traceback.format_exc()}" + + with open(logfile, "a") as file: + file.write(status + "\n") + + +if __name__ == "__main__": + batch_convert() diff --git a/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py b/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py new file mode 100644 index 0000000000000000000000000000000000000000..6bc9f308002451af50592e455ca2233be2d2dc00 --- /dev/null +++ b/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py @@ -0,0 +1,114 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script will help you convert any LeRobot dataset already pushed to the hub from codebase version 2.0 to +2.1. It will: + +- Generate per-episodes stats and writes them in `episodes_stats.jsonl` +- Check consistency between these new stats and the old ones. +- Remove the deprecated `stats.json`. +- Update codebase_version in `info.json`. +- Push this new version to the hub on the 'main' branch and tags it with "v2.1". + +Usage: + +```bash +python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py \ + --repo-id=aliberts/koch_tutorial +``` + +""" + +import argparse +import logging + +from huggingface_hub import HfApi + +from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset +from lerobot.common.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info +from lerobot.common.datasets.v21.convert_stats import check_aggregate_stats, convert_stats + +V20 = "v2.0" +V21 = "v2.1" + + +class SuppressWarnings: + def __enter__(self): + self.previous_level = logging.getLogger().getEffectiveLevel() + logging.getLogger().setLevel(logging.ERROR) + + def __exit__(self, exc_type, exc_val, exc_tb): + logging.getLogger().setLevel(self.previous_level) + + +def convert_dataset( + repo_id: str, + branch: str | None = None, + num_workers: int = 4, +): + with SuppressWarnings(): + dataset = LeRobotDataset(repo_id, revision=V20, force_cache_sync=True) + + if (dataset.root / EPISODES_STATS_PATH).is_file(): + (dataset.root / EPISODES_STATS_PATH).unlink() + + convert_stats(dataset, num_workers=num_workers) + ref_stats = load_stats(dataset.root) + check_aggregate_stats(dataset, ref_stats) + + dataset.meta.info["codebase_version"] = CODEBASE_VERSION + write_info(dataset.meta.info, dataset.root) + + dataset.push_to_hub(branch=branch, tag_version=False, allow_patterns="meta/") + + # delete old stats.json file + if (dataset.root / STATS_PATH).is_file: + (dataset.root / STATS_PATH).unlink() + + hub_api = HfApi() + if hub_api.file_exists( + repo_id=dataset.repo_id, filename=STATS_PATH, revision=branch, repo_type="dataset" + ): + hub_api.delete_file( + path_in_repo=STATS_PATH, repo_id=dataset.repo_id, revision=branch, repo_type="dataset" + ) + + hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--repo-id", + type=str, + required=True, + help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset " + "(e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).", + ) + parser.add_argument( + "--branch", + type=str, + default=None, + help="Repo branch to push your dataset. Defaults to the main branch.", + ) + parser.add_argument( + "--num-workers", + type=int, + default=4, + help="Number of workers for parallelizing stats compute. Defaults to 4.", + ) + + args = parser.parse_args() + convert_dataset(**vars(args)) diff --git a/lerobot/common/datasets/v21/convert_stats.py b/lerobot/common/datasets/v21/convert_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..dfb71e772d62f8d362926702bcf01e008b1cbd44 --- /dev/null +++ b/lerobot/common/datasets/v21/convert_stats.py @@ -0,0 +1,99 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from concurrent.futures import ThreadPoolExecutor, as_completed + +import numpy as np +from tqdm import tqdm + +from lerobot.common.datasets.compute_stats import aggregate_stats, get_feature_stats, sample_indices +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.utils import write_episode_stats + + +def sample_episode_video_frames(dataset: LeRobotDataset, episode_index: int, ft_key: str) -> np.ndarray: + ep_len = dataset.meta.episodes[episode_index]["length"] + sampled_indices = sample_indices(ep_len) + query_timestamps = dataset._get_query_timestamps(0.0, {ft_key: sampled_indices}) + video_frames = dataset._query_videos(query_timestamps, episode_index) + return video_frames[ft_key].numpy() + + +def convert_episode_stats(dataset: LeRobotDataset, ep_idx: int): + ep_start_idx = dataset.episode_data_index["from"][ep_idx] + ep_end_idx = dataset.episode_data_index["to"][ep_idx] + ep_data = dataset.hf_dataset.select(range(ep_start_idx, ep_end_idx)) + + ep_stats = {} + for key, ft in dataset.features.items(): + if ft["dtype"] == "video": + # We sample only for videos + ep_ft_data = sample_episode_video_frames(dataset, ep_idx, key) + else: + ep_ft_data = np.array(ep_data[key]) + + axes_to_reduce = (0, 2, 3) if ft["dtype"] in ["image", "video"] else 0 + keepdims = True if ft["dtype"] in ["image", "video"] else ep_ft_data.ndim == 1 + ep_stats[key] = get_feature_stats(ep_ft_data, axis=axes_to_reduce, keepdims=keepdims) + + if ft["dtype"] in ["image", "video"]: # remove batch dim + ep_stats[key] = { + k: v if k == "count" else np.squeeze(v, axis=0) for k, v in ep_stats[key].items() + } + + dataset.meta.episodes_stats[ep_idx] = ep_stats + + +def convert_stats(dataset: LeRobotDataset, num_workers: int = 0): + assert dataset.episodes is None + print("Computing episodes stats") + total_episodes = dataset.meta.total_episodes + if num_workers > 0: + with ThreadPoolExecutor(max_workers=num_workers) as executor: + futures = { + executor.submit(convert_episode_stats, dataset, ep_idx): ep_idx + for ep_idx in range(total_episodes) + } + for future in tqdm(as_completed(futures), total=total_episodes): + future.result() + else: + for ep_idx in tqdm(range(total_episodes)): + convert_episode_stats(dataset, ep_idx) + + for ep_idx in tqdm(range(total_episodes)): + write_episode_stats(ep_idx, dataset.meta.episodes_stats[ep_idx], dataset.root) + + +def check_aggregate_stats( + dataset: LeRobotDataset, + reference_stats: dict[str, dict[str, np.ndarray]], + video_rtol_atol: tuple[float] = (1e-2, 1e-2), + default_rtol_atol: tuple[float] = (5e-6, 6e-5), +): + """Verifies that the aggregated stats from episodes_stats are close to reference stats.""" + agg_stats = aggregate_stats(list(dataset.meta.episodes_stats.values())) + for key, ft in dataset.features.items(): + # These values might need some fine-tuning + if ft["dtype"] == "video": + # to account for image sub-sampling + rtol, atol = video_rtol_atol + else: + rtol, atol = default_rtol_atol + + for stat, val in agg_stats[key].items(): + if key in reference_stats and stat in reference_stats[key]: + err_msg = f"feature='{key}' stats='{stat}'" + np.testing.assert_allclose( + val, reference_stats[key][stat], rtol=rtol, atol=atol, err_msg=err_msg + ) diff --git a/lerobot/common/datasets/video_utils.py b/lerobot/common/datasets/video_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..42b2f7142d61941748ce46aead9bbe57c76e6a33 --- /dev/null +++ b/lerobot/common/datasets/video_utils.py @@ -0,0 +1,453 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import glob +import importlib +import logging +import warnings +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, ClassVar + +import av +import pyarrow as pa +import torch +import torchvision +from datasets.features.features import register_feature +from PIL import Image + + +def get_safe_default_codec(): + if importlib.util.find_spec("torchcodec"): + return "torchcodec" + else: + logging.warning( + "'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder" + ) + return "pyav" + + +def decode_video_frames( + video_path: Path | str, + timestamps: list[float], + tolerance_s: float, + backend: str | None = None, +) -> torch.Tensor: + """ + Decodes video frames using the specified backend. + + Args: + video_path (Path): Path to the video file. + timestamps (list[float]): List of timestamps to extract frames. + tolerance_s (float): Allowed deviation in seconds for frame retrieval. + backend (str, optional): Backend to use for decoding. Defaults to "torchcodec" when available in the platform; otherwise, defaults to "pyav".. + + Returns: + torch.Tensor: Decoded frames. + + Currently supports torchcodec on cpu and pyav. + """ + if backend is None: + backend = get_safe_default_codec() + if backend == "torchcodec": + return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s) + elif backend in ["pyav", "video_reader"]: + return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend) + else: + raise ValueError(f"Unsupported video backend: {backend}") + + +def decode_video_frames_torchvision( + video_path: Path | str, + timestamps: list[float], + tolerance_s: float, + backend: str = "pyav", + log_loaded_timestamps: bool = False, +) -> torch.Tensor: + """Loads frames associated to the requested timestamps of a video + + The backend can be either "pyav" (default) or "video_reader". + "video_reader" requires installing torchvision from source, see: + https://github.com/pytorch/vision/blob/main/torchvision/csrc/io/decoder/gpu/README.rst + (note that you need to compile against ffmpeg<4.3) + + While both use cpu, "video_reader" is supposedly faster than "pyav" but requires additional setup. + For more info on video decoding, see `benchmark/video/README.md` + + See torchvision doc for more info on these two backends: + https://pytorch.org/vision/0.18/index.html?highlight=backend#torchvision.set_video_backend + + Note: Video benefits from inter-frame compression. Instead of storing every frame individually, + the encoder stores a reference frame (or a key frame) and subsequent frames as differences relative to + that key frame. As a consequence, to access a requested frame, we need to load the preceding key frame, + and all subsequent frames until reaching the requested frame. The number of key frames in a video + can be adjusted during encoding to take into account decoding time and video size in bytes. + """ + video_path = str(video_path) + + # set backend + keyframes_only = False + torchvision.set_video_backend(backend) + if backend == "pyav": + keyframes_only = True # pyav doesn't support accurate seek + + # set a video stream reader + # TODO(rcadene): also load audio stream at the same time + reader = torchvision.io.VideoReader(video_path, "video") + + # set the first and last requested timestamps + # Note: previous timestamps are usually loaded, since we need to access the previous key frame + first_ts = min(timestamps) + last_ts = max(timestamps) + + # access closest key frame of the first requested frame + # Note: closest key frame timestamp is usually smaller than `first_ts` (e.g. key frame can be the first frame of the video) + # for details on what `seek` is doing see: https://pyav.basswood-io.com/docs/stable/api/container.html?highlight=inputcontainer#av.container.InputContainer.seek + reader.seek(first_ts, keyframes_only=keyframes_only) + + # load all frames until last requested frame + loaded_frames = [] + loaded_ts = [] + for frame in reader: + current_ts = frame["pts"] + if log_loaded_timestamps: + logging.info(f"frame loaded at timestamp={current_ts:.4f}") + loaded_frames.append(frame["data"]) + loaded_ts.append(current_ts) + if current_ts >= last_ts: + break + + if backend == "pyav": + reader.container.close() + + reader = None + + query_ts = torch.tensor(timestamps) + loaded_ts = torch.tensor(loaded_ts) + + # compute distances between each query timestamp and timestamps of all loaded frames + dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1) + min_, argmin_ = dist.min(1) + + is_within_tol = min_ < tolerance_s + assert is_within_tol.all(), ( + f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})." + "It means that the closest frame that can be loaded from the video is too far away in time." + "This might be due to synchronization issues with timestamps during data collection." + "To be safe, we advise to ignore this item during training." + f"\nqueried timestamps: {query_ts}" + f"\nloaded timestamps: {loaded_ts}" + f"\nvideo: {video_path}" + f"\nbackend: {backend}" + ) + + # get closest frames to the query timestamps + closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_]) + closest_ts = loaded_ts[argmin_] + + if log_loaded_timestamps: + logging.info(f"{closest_ts=}") + + # convert to the pytorch format which is float32 in [0,1] range (and channel first) + closest_frames = closest_frames.type(torch.float32) / 255 + + assert len(timestamps) == len(closest_frames) + return closest_frames + + +def decode_video_frames_torchcodec( + video_path: Path | str, + timestamps: list[float], + tolerance_s: float, + device: str = "cpu", + log_loaded_timestamps: bool = False, +) -> torch.Tensor: + """Loads frames associated with the requested timestamps of a video using torchcodec. + + Note: Setting device="cuda" outside the main process, e.g. in data loader workers, will lead to CUDA initialization errors. + + Note: Video benefits from inter-frame compression. Instead of storing every frame individually, + the encoder stores a reference frame (or a key frame) and subsequent frames as differences relative to + that key frame. As a consequence, to access a requested frame, we need to load the preceding key frame, + and all subsequent frames until reaching the requested frame. The number of key frames in a video + can be adjusted during encoding to take into account decoding time and video size in bytes. + """ + + if importlib.util.find_spec("torchcodec"): + from torchcodec.decoders import VideoDecoder + else: + raise ImportError("torchcodec is required but not available.") + + # initialize video decoder + decoder = VideoDecoder(video_path, device=device, seek_mode="approximate") + loaded_frames = [] + loaded_ts = [] + # get metadata for frame information + metadata = decoder.metadata + average_fps = metadata.average_fps + + # convert timestamps to frame indices + frame_indices = [round(ts * average_fps) for ts in timestamps] + + # retrieve frames based on indices + frames_batch = decoder.get_frames_at(indices=frame_indices) + + for frame, pts in zip(frames_batch.data, frames_batch.pts_seconds, strict=False): + loaded_frames.append(frame) + loaded_ts.append(pts.item()) + if log_loaded_timestamps: + logging.info(f"Frame loaded at timestamp={pts:.4f}") + + query_ts = torch.tensor(timestamps) + loaded_ts = torch.tensor(loaded_ts) + + # compute distances between each query timestamp and loaded timestamps + dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1) + min_, argmin_ = dist.min(1) + + is_within_tol = min_ < tolerance_s + assert is_within_tol.all(), ( + f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})." + "It means that the closest frame that can be loaded from the video is too far away in time." + "This might be due to synchronization issues with timestamps during data collection." + "To be safe, we advise to ignore this item during training." + f"\nqueried timestamps: {query_ts}" + f"\nloaded timestamps: {loaded_ts}" + f"\nvideo: {video_path}" + ) + + # get closest frames to the query timestamps + closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_]) + closest_ts = loaded_ts[argmin_] + + if log_loaded_timestamps: + logging.info(f"{closest_ts=}") + + # convert to float32 in [0,1] range (channel first) + closest_frames = closest_frames.type(torch.float32) / 255 + + assert len(timestamps) == len(closest_frames) + return closest_frames + + +def encode_video_frames( + imgs_dir: Path | str, + video_path: Path | str, + fps: int, + vcodec: str = "libsvtav1", + pix_fmt: str = "yuv420p", + g: int | None = 2, + crf: int | None = 30, + fast_decode: int = 0, + log_level: int | None = av.logging.ERROR, + overwrite: bool = False, +) -> None: + """More info on ffmpeg arguments tuning on `benchmark/video/README.md`""" + # Check encoder availability + if vcodec not in ["h264", "hevc", "libsvtav1"]: + raise ValueError(f"Unsupported video codec: {vcodec}. Supported codecs are: h264, hevc, libsvtav1.") + + video_path = Path(video_path) + imgs_dir = Path(imgs_dir) + + video_path.parent.mkdir(parents=True, exist_ok=overwrite) + + # Encoders/pixel formats incompatibility check + if (vcodec == "libsvtav1" or vcodec == "hevc") and pix_fmt == "yuv444p": + logging.warning( + f"Incompatible pixel format 'yuv444p' for codec {vcodec}, auto-selecting format 'yuv420p'" + ) + pix_fmt = "yuv420p" + + # Get input frames + template = "frame_" + ("[0-9]" * 6) + ".png" + input_list = sorted( + glob.glob(str(imgs_dir / template)), key=lambda x: int(x.split("_")[-1].split(".")[0]) + ) + + # Define video output frame size (assuming all input frames are the same size) + if len(input_list) == 0: + raise FileNotFoundError(f"No images found in {imgs_dir}.") + dummy_image = Image.open(input_list[0]) + width, height = dummy_image.size + + # Define video codec options + video_options = {} + + if g is not None: + video_options["g"] = str(g) + + if crf is not None: + video_options["crf"] = str(crf) + + if fast_decode: + key = "svtav1-params" if vcodec == "libsvtav1" else "tune" + value = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode" + video_options[key] = value + + # Set logging level + if log_level is not None: + # "While less efficient, it is generally preferable to modify logging with Python’s logging" + logging.getLogger("libav").setLevel(log_level) + + # Create and open output file (overwrite by default) + with av.open(str(video_path), "w") as output: + output_stream = output.add_stream(vcodec, fps, options=video_options) + output_stream.pix_fmt = pix_fmt + output_stream.width = width + output_stream.height = height + + # Loop through input frames and encode them + for input_data in input_list: + input_image = Image.open(input_data).convert("RGB") + input_frame = av.VideoFrame.from_image(input_image) + packet = output_stream.encode(input_frame) + if packet: + output.mux(packet) + + # Flush the encoder + packet = output_stream.encode() + if packet: + output.mux(packet) + + # Reset logging level + if log_level is not None: + av.logging.restore_default_callback() + + if not video_path.exists(): + raise OSError(f"Video encoding did not work. File not found: {video_path}.") + + +@dataclass +class VideoFrame: + # TODO(rcadene, lhoestq): move to Hugging Face `datasets` repo + """ + Provides a type for a dataset containing video frames. + + Example: + + ```python + data_dict = [{"image": {"path": "videos/episode_0.mp4", "timestamp": 0.3}}] + features = {"image": VideoFrame()} + Dataset.from_dict(data_dict, features=Features(features)) + ``` + """ + + pa_type: ClassVar[Any] = pa.struct({"path": pa.string(), "timestamp": pa.float32()}) + _type: str = field(default="VideoFrame", init=False, repr=False) + + def __call__(self): + return self.pa_type + + +with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + "'register_feature' is experimental and might be subject to breaking changes in the future.", + category=UserWarning, + ) + # to make VideoFrame available in HuggingFace `datasets` + register_feature(VideoFrame, "VideoFrame") + + +def get_audio_info(video_path: Path | str) -> dict: + # Set logging level + logging.getLogger("libav").setLevel(av.logging.ERROR) + + # Getting audio stream information + audio_info = {} + with av.open(str(video_path), "r") as audio_file: + try: + audio_stream = audio_file.streams.audio[0] + except IndexError: + # Reset logging level + av.logging.restore_default_callback() + return {"has_audio": False} + + audio_info["audio.channels"] = audio_stream.channels + audio_info["audio.codec"] = audio_stream.codec.canonical_name + # In an ideal loseless case : bit depth x sample rate x channels = bit rate. + # In an actual compressed case, the bit rate is set according to the compression level : the lower the bit rate, the more compression is applied. + audio_info["audio.bit_rate"] = audio_stream.bit_rate + audio_info["audio.sample_rate"] = audio_stream.sample_rate # Number of samples per second + # In an ideal loseless case : fixed number of bits per sample. + # In an actual compressed case : variable number of bits per sample (often reduced to match a given depth rate). + audio_info["audio.bit_depth"] = audio_stream.format.bits + audio_info["audio.channel_layout"] = audio_stream.layout.name + audio_info["has_audio"] = True + + # Reset logging level + av.logging.restore_default_callback() + + return audio_info + + +def get_video_info(video_path: Path | str) -> dict: + # Set logging level + logging.getLogger("libav").setLevel(av.logging.ERROR) + + # Getting video stream information + video_info = {} + with av.open(str(video_path), "r") as video_file: + try: + video_stream = video_file.streams.video[0] + except IndexError: + # Reset logging level + av.logging.restore_default_callback() + return {} + + video_info["video.height"] = video_stream.height + video_info["video.width"] = video_stream.width + video_info["video.codec"] = video_stream.codec.canonical_name + video_info["video.pix_fmt"] = video_stream.pix_fmt + video_info["video.is_depth_map"] = False + + # Calculate fps from r_frame_rate + video_info["video.fps"] = int(video_stream.base_rate) + + pixel_channels = get_video_pixel_channels(video_stream.pix_fmt) + video_info["video.channels"] = pixel_channels + + # Reset logging level + av.logging.restore_default_callback() + + # Adding audio stream information + video_info.update(**get_audio_info(video_path)) + + return video_info + + +def get_video_pixel_channels(pix_fmt: str) -> int: + if "gray" in pix_fmt or "depth" in pix_fmt or "monochrome" in pix_fmt: + return 1 + elif "rgba" in pix_fmt or "yuva" in pix_fmt: + return 4 + elif "rgb" in pix_fmt or "yuv" in pix_fmt: + return 3 + else: + raise ValueError("Unknown format") + + +def get_image_pixel_channels(image: Image): + if image.mode == "L": + return 1 # Grayscale + elif image.mode == "LA": + return 2 # Grayscale + Alpha + elif image.mode == "RGB": + return 3 # RGB + elif image.mode == "RGBA": + return 4 # RGBA + else: + raise ValueError("Unknown format") diff --git a/lerobot/common/envs/__init__.py b/lerobot/common/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a2d11463f5efd685b4507c18f48164aa36f02320 --- /dev/null +++ b/lerobot/common/envs/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .configs import AlohaEnv, EnvConfig, PushtEnv, XarmEnv # noqa: F401 diff --git a/lerobot/common/envs/configs.py b/lerobot/common/envs/configs.py new file mode 100644 index 0000000000000000000000000000000000000000..a2b4b84d49593440160c681f443a961e4044190e --- /dev/null +++ b/lerobot/common/envs/configs.py @@ -0,0 +1,273 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from dataclasses import dataclass, field +from typing import Any, Optional + +import draccus + +from lerobot.common.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE +from lerobot.common.robots import RobotConfig +from lerobot.common.teleoperators.config import TeleoperatorConfig +from lerobot.configs.types import FeatureType, PolicyFeature + + +@dataclass +class EnvConfig(draccus.ChoiceRegistry, abc.ABC): + task: str | None = None + fps: int = 30 + features: dict[str, PolicyFeature] = field(default_factory=dict) + features_map: dict[str, str] = field(default_factory=dict) + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) + + @property + @abc.abstractmethod + def gym_kwargs(self) -> dict: + raise NotImplementedError() + + +@EnvConfig.register_subclass("aloha") +@dataclass +class AlohaEnv(EnvConfig): + task: str = "AlohaInsertion-v0" + fps: int = 50 + episode_length: int = 400 + obs_type: str = "pixels_agent_pos" + render_mode: str = "rgb_array" + features: dict[str, PolicyFeature] = field( + default_factory=lambda: { + "action": PolicyFeature(type=FeatureType.ACTION, shape=(14,)), + } + ) + features_map: dict[str, str] = field( + default_factory=lambda: { + "action": ACTION, + "agent_pos": OBS_STATE, + "top": f"{OBS_IMAGE}.top", + "pixels/top": f"{OBS_IMAGES}.top", + } + ) + + def __post_init__(self): + if self.obs_type == "pixels": + self.features["top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 640, 3)) + elif self.obs_type == "pixels_agent_pos": + self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(14,)) + self.features["pixels/top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 640, 3)) + + @property + def gym_kwargs(self) -> dict: + return { + "obs_type": self.obs_type, + "render_mode": self.render_mode, + "max_episode_steps": self.episode_length, + } + + +@EnvConfig.register_subclass("pusht") +@dataclass +class PushtEnv(EnvConfig): + task: str = "PushT-v0" + fps: int = 10 + episode_length: int = 300 + obs_type: str = "pixels_agent_pos" + render_mode: str = "rgb_array" + visualization_width: int = 384 + visualization_height: int = 384 + features: dict[str, PolicyFeature] = field( + default_factory=lambda: { + "action": PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + "agent_pos": PolicyFeature(type=FeatureType.STATE, shape=(2,)), + } + ) + features_map: dict[str, str] = field( + default_factory=lambda: { + "action": ACTION, + "agent_pos": OBS_STATE, + "environment_state": OBS_ENV_STATE, + "pixels": OBS_IMAGE, + } + ) + + def __post_init__(self): + if self.obs_type == "pixels_agent_pos": + self.features["pixels"] = PolicyFeature(type=FeatureType.VISUAL, shape=(384, 384, 3)) + elif self.obs_type == "environment_state_agent_pos": + self.features["environment_state"] = PolicyFeature(type=FeatureType.ENV, shape=(16,)) + + @property + def gym_kwargs(self) -> dict: + return { + "obs_type": self.obs_type, + "render_mode": self.render_mode, + "visualization_width": self.visualization_width, + "visualization_height": self.visualization_height, + "max_episode_steps": self.episode_length, + } + + +@EnvConfig.register_subclass("xarm") +@dataclass +class XarmEnv(EnvConfig): + task: str = "XarmLift-v0" + fps: int = 15 + episode_length: int = 200 + obs_type: str = "pixels_agent_pos" + render_mode: str = "rgb_array" + visualization_width: int = 384 + visualization_height: int = 384 + features: dict[str, PolicyFeature] = field( + default_factory=lambda: { + "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)), + "pixels": PolicyFeature(type=FeatureType.VISUAL, shape=(84, 84, 3)), + } + ) + features_map: dict[str, str] = field( + default_factory=lambda: { + "action": ACTION, + "agent_pos": OBS_STATE, + "pixels": OBS_IMAGE, + } + ) + + def __post_init__(self): + if self.obs_type == "pixels_agent_pos": + self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(4,)) + + @property + def gym_kwargs(self) -> dict: + return { + "obs_type": self.obs_type, + "render_mode": self.render_mode, + "visualization_width": self.visualization_width, + "visualization_height": self.visualization_height, + "max_episode_steps": self.episode_length, + } + + +@dataclass +class VideoRecordConfig: + """Configuration for video recording in ManiSkill environments.""" + + enabled: bool = False + record_dir: str = "videos" + trajectory_name: str = "trajectory" + + +@dataclass +class EnvTransformConfig: + """Configuration for environment wrappers.""" + + # ee_action_space_params: EEActionSpaceConfig = field(default_factory=EEActionSpaceConfig) + control_mode: str = "gamepad" + display_cameras: bool = False + add_joint_velocity_to_observation: bool = False + add_current_to_observation: bool = False + add_ee_pose_to_observation: bool = False + crop_params_dict: Optional[dict[str, tuple[int, int, int, int]]] = None + resize_size: Optional[tuple[int, int]] = None + control_time_s: float = 20.0 + fixed_reset_joint_positions: Optional[Any] = None + reset_time_s: float = 5.0 + use_gripper: bool = True + gripper_quantization_threshold: float | None = 0.8 + gripper_penalty: float = 0.0 + gripper_penalty_in_reward: bool = False + + +@EnvConfig.register_subclass(name="gym_manipulator") +@dataclass +class HILSerlRobotEnvConfig(EnvConfig): + """Configuration for the HILSerlRobotEnv environment.""" + + robot: Optional[RobotConfig] = None + teleop: Optional[TeleoperatorConfig] = None + wrapper: Optional[EnvTransformConfig] = None + fps: int = 10 + name: str = "real_robot" + mode: str = None # Either "record", "replay", None + repo_id: Optional[str] = None + dataset_root: Optional[str] = None + task: str = "" + num_episodes: int = 10 # only for record mode + episode: int = 0 + device: str = "cuda" + push_to_hub: bool = True + pretrained_policy_name_or_path: Optional[str] = None + reward_classifier_pretrained_path: Optional[str] = None + # For the reward classifier, to record more positive examples after a success + number_of_steps_after_success: int = 0 + + def gym_kwargs(self) -> dict: + return {} + + +@EnvConfig.register_subclass("hil") +@dataclass +class HILEnvConfig(EnvConfig): + """Configuration for the HIL environment.""" + + type: str = "hil" + name: str = "PandaPickCube" + task: str = "PandaPickCubeKeyboard-v0" + use_viewer: bool = True + gripper_penalty: float = 0.0 + use_gamepad: bool = True + state_dim: int = 18 + action_dim: int = 4 + fps: int = 100 + episode_length: int = 100 + video_record: VideoRecordConfig = field(default_factory=VideoRecordConfig) + features: dict[str, PolicyFeature] = field( + default_factory=lambda: { + "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)), + "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(18,)), + } + ) + features_map: dict[str, str] = field( + default_factory=lambda: { + "action": ACTION, + "observation.image": OBS_IMAGE, + "observation.state": OBS_STATE, + } + ) + ################# args from hilserlrobotenv + reward_classifier_pretrained_path: Optional[str] = None + robot_config: Optional[RobotConfig] = None + teleop_config: Optional[TeleoperatorConfig] = None + wrapper: Optional[EnvTransformConfig] = None + mode: str = None # Either "record", "replay", None + repo_id: Optional[str] = None + dataset_root: Optional[str] = None + num_episodes: int = 10 # only for record mode + episode: int = 0 + device: str = "cuda" + push_to_hub: bool = True + pretrained_policy_name_or_path: Optional[str] = None + # For the reward classifier, to record more positive examples after a success + number_of_steps_after_success: int = 0 + ############################ + + @property + def gym_kwargs(self) -> dict: + return { + "use_viewer": self.use_viewer, + "use_gamepad": self.use_gamepad, + "gripper_penalty": self.gripper_penalty, + } diff --git a/lerobot/common/envs/factory.py b/lerobot/common/envs/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..2dfbb4854dac7ce52cb8b7f5e02506ae56d14d4a --- /dev/null +++ b/lerobot/common/envs/factory.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib + +import gymnasium as gym + +from lerobot.common.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv + + +def make_env_config(env_type: str, **kwargs) -> EnvConfig: + if env_type == "aloha": + return AlohaEnv(**kwargs) + elif env_type == "pusht": + return PushtEnv(**kwargs) + elif env_type == "xarm": + return XarmEnv(**kwargs) + elif env_type == "hil": + return HILEnvConfig(**kwargs) + else: + raise ValueError(f"Policy type '{env_type}' is not available.") + + +def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> gym.vector.VectorEnv | None: + """Makes a gym vector environment according to the config. + + Args: + cfg (EnvConfig): the config of the environment to instantiate. + n_envs (int, optional): The number of parallelized env to return. Defaults to 1. + use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to + False. + + Raises: + ValueError: if n_envs < 1 + ModuleNotFoundError: If the requested env package is not installed + + Returns: + gym.vector.VectorEnv: The parallelized gym.env instance. + """ + if n_envs < 1: + raise ValueError("`n_envs must be at least 1") + + package_name = f"gym_{cfg.type}" + + try: + importlib.import_module(package_name) + except ModuleNotFoundError as e: + print(f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.type}]'`") + raise e + + gym_handle = f"{package_name}/{cfg.task}" + + # batched version of the env that returns an observation of shape (b, c) + env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv + env = env_cls( + [lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs) for _ in range(n_envs)] + ) + + return env diff --git a/lerobot/common/envs/utils.py b/lerobot/common/envs/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cbe36082fe47f404b8d059fc6df1b67ed10bd35c --- /dev/null +++ b/lerobot/common/envs/utils.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import warnings +from typing import Any + +import einops +import gymnasium as gym +import numpy as np +import torch +from torch import Tensor + +from lerobot.common.envs.configs import EnvConfig +from lerobot.common.utils.utils import get_channel_first_image_shape +from lerobot.configs.types import FeatureType, PolicyFeature + + +def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Tensor]: + # TODO(aliberts, rcadene): refactor this to use features from the environment (no hardcoding) + """Convert environment observation to LeRobot format observation. + Args: + observation: Dictionary of observation batches from a Gym vector environment. + Returns: + Dictionary of observation batches with keys renamed to LeRobot format and values as tensors. + """ + # map to expected inputs for the policy + return_observations = {} + if "pixels" in observations: + if isinstance(observations["pixels"], dict): + imgs = {f"observation.images.{key}": img for key, img in observations["pixels"].items()} + else: + imgs = {"observation.image": observations["pixels"]} + + for imgkey, img in imgs.items(): + # TODO(aliberts, rcadene): use transforms.ToTensor()? + img = torch.from_numpy(img) + + # When preprocessing observations in a non-vectorized environment, we need to add a batch dimension. + # This is the case for human-in-the-loop RL where there is only one environment. + if img.ndim == 3: + img = img.unsqueeze(0) + # sanity check that images are channel last + _, h, w, c = img.shape + assert c < h and c < w, f"expect channel last images, but instead got {img.shape=}" + + # sanity check that images are uint8 + assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}" + + # convert to channel first of type float32 in range [0,1] + img = einops.rearrange(img, "b h w c -> b c h w").contiguous() + img = img.type(torch.float32) + img /= 255 + + return_observations[imgkey] = img + + if "environment_state" in observations: + env_state = torch.from_numpy(observations["environment_state"]).float() + if env_state.dim() == 1: + env_state = env_state.unsqueeze(0) + + return_observations["observation.environment_state"] = env_state + + # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing + agent_pos = torch.from_numpy(observations["agent_pos"]).float() + if agent_pos.dim() == 1: + agent_pos = agent_pos.unsqueeze(0) + return_observations["observation.state"] = agent_pos + + return return_observations + + +def env_to_policy_features(env_cfg: EnvConfig) -> dict[str, PolicyFeature]: + # TODO(aliberts, rcadene): remove this hardcoding of keys and just use the nested keys as is + # (need to also refactor preprocess_observation and externalize normalization from policies) + policy_features = {} + for key, ft in env_cfg.features.items(): + if ft.type is FeatureType.VISUAL: + if len(ft.shape) != 3: + raise ValueError(f"Number of dimensions of {key} != 3 (shape={ft.shape})") + + shape = get_channel_first_image_shape(ft.shape) + feature = PolicyFeature(type=ft.type, shape=shape) + else: + feature = ft + + policy_key = env_cfg.features_map[key] + policy_features[policy_key] = feature + + return policy_features + + +def are_all_envs_same_type(env: gym.vector.VectorEnv) -> bool: + first_type = type(env.envs[0]) # Get type of first env + return all(type(e) is first_type for e in env.envs) # Fast type check + + +def check_env_attributes_and_types(env: gym.vector.VectorEnv) -> None: + with warnings.catch_warnings(): + warnings.simplefilter("once", UserWarning) # Apply filter only in this function + + if not (hasattr(env.envs[0], "task_description") and hasattr(env.envs[0], "task")): + warnings.warn( + "The environment does not have 'task_description' and 'task'. Some policies require these features.", + UserWarning, + stacklevel=2, + ) + if not are_all_envs_same_type(env): + warnings.warn( + "The environments have different types. Make sure you infer the right task from each environment. Empty task will be passed instead.", + UserWarning, + stacklevel=2, + ) + + +def add_envs_task(env: gym.vector.VectorEnv, observation: dict[str, Any]) -> dict[str, Any]: + """Adds task feature to the observation dict with respect to the first environment attribute.""" + if hasattr(env.envs[0], "task_description"): + observation["task"] = env.call("task_description") + elif hasattr(env.envs[0], "task"): + observation["task"] = env.call("task") + else: # For envs without language instructions, e.g. aloha transfer cube and etc. + num_envs = observation[list(observation.keys())[0]].shape[0] + observation["task"] = ["" for _ in range(num_envs)] + return observation diff --git a/lerobot/common/errors.py b/lerobot/common/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..48de4e59b132c86f56bc607bf3323a2d8f4f4e32 --- /dev/null +++ b/lerobot/common/errors.py @@ -0,0 +1,43 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class DeviceNotConnectedError(ConnectionError): + """Exception raised when the device is not connected.""" + + def __init__(self, message="This device is not connected. Try calling `connect()` first."): + self.message = message + super().__init__(self.message) + + +class DeviceAlreadyConnectedError(ConnectionError): + """Exception raised when the device is already connected.""" + + def __init__( + self, + message="This device is already connected. Try not calling `connect()` twice.", + ): + self.message = message + super().__init__(self.message) + + +class InvalidActionError(ValueError): + """Exception raised when an action is already invalid.""" + + def __init__( + self, + message="The action is invalid. Check the value follows what it is expected from the action space.", + ): + self.message = message + super().__init__(self.message) diff --git a/lerobot/common/model/kinematics.py b/lerobot/common/model/kinematics.py new file mode 100644 index 0000000000000000000000000000000000000000..b3dc88389783c9cda5ea289e27b0f9c68dd0ee63 --- /dev/null +++ b/lerobot/common/model/kinematics.py @@ -0,0 +1,483 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +from numpy.typing import NDArray +from scipy.spatial.transform import Rotation + + +def skew_symmetric(w: NDArray[np.float32]) -> NDArray[np.float32]: + """Creates the skew-symmetric matrix from a 3D vector.""" + return np.array([[0, -w[2], w[1]], [w[2], 0, -w[0]], [-w[1], w[0], 0]]) + + +def rodrigues_rotation(w: NDArray[np.float32], theta: float) -> NDArray[np.float32]: + """Computes the rotation matrix using Rodrigues' formula.""" + w_hat = skew_symmetric(w) + return np.eye(3) + np.sin(theta) * w_hat + (1 - np.cos(theta)) * w_hat @ w_hat + + +def screw_axis_to_transform(s: NDArray[np.float32], theta: float) -> NDArray[np.float32]: + """Converts a screw axis to a 4x4 transformation matrix.""" + screw_axis_rot = s[:3] + screw_axis_trans = s[3:] + + # Pure translation + if np.allclose(screw_axis_rot, 0) and np.linalg.norm(screw_axis_trans) == 1: + transform = np.eye(4) + transform[:3, 3] = screw_axis_trans * theta + + # Rotation (and potentially translation) + elif np.linalg.norm(screw_axis_rot) == 1: + w_hat = skew_symmetric(screw_axis_rot) + rot_mat = np.eye(3) + np.sin(theta) * w_hat + (1 - np.cos(theta)) * w_hat @ w_hat + t = ( + np.eye(3) * theta + (1 - np.cos(theta)) * w_hat + (theta - np.sin(theta)) * w_hat @ w_hat + ) @ screw_axis_trans + transform = np.eye(4) + transform[:3, :3] = rot_mat + transform[:3, 3] = t + else: + raise ValueError("Invalid screw axis parameters") + return transform + + +def pose_difference_se3(pose1: NDArray[np.float32], pose2: NDArray[np.float32]) -> NDArray[np.float32]: + """ + Calculates the SE(3) difference between two 4x4 homogeneous transformation matrices. + SE(3) (Special Euclidean Group) represents rigid body transformations in 3D space, + combining rotation (SO(3)) and translation. + + Each 4x4 matrix has the following structure: + [R11 R12 R13 tx] + [R21 R22 R23 ty] + [R31 R32 R33 tz] + [ 0 0 0 1] + + where R is the 3x3 rotation matrix and [tx,ty,tz] is the translation vector. + + Args: + pose1: A 4x4 numpy array representing the first pose. + pose2: A 4x4 numpy array representing the second pose. + + Returns: + A 6D numpy array concatenating translation and rotation differences. + First 3 elements are the translational difference (position). + Last 3 elements are the rotational difference in axis-angle representation. + """ + rot1 = pose1[:3, :3] + rot2 = pose2[:3, :3] + + translation_diff = pose1[:3, 3] - pose2[:3, 3] + + # Calculate rotational difference using scipy's Rotation library + rot_diff = Rotation.from_matrix(rot1 @ rot2.T) + rotation_diff = rot_diff.as_rotvec() # Axis-angle representation + + return np.concatenate([translation_diff, rotation_diff]) + + +def se3_error(target_pose: NDArray[np.float32], current_pose: NDArray[np.float32]) -> NDArray[np.float32]: + pos_error = target_pose[:3, 3] - current_pose[:3, 3] + + rot_target = target_pose[:3, :3] + rot_current = current_pose[:3, :3] + rot_error_mat = rot_target @ rot_current.T + rot_error = Rotation.from_matrix(rot_error_mat).as_rotvec() + + return np.concatenate([pos_error, rot_error]) + + +class RobotKinematics: + """Robot kinematics class supporting multiple robot models.""" + + # Robot measurements dictionary + ROBOT_MEASUREMENTS = { + "koch": { + "gripper": [0.239, -0.001, 0.024], + "wrist": [0.209, 0, 0.024], + "forearm": [0.108, 0, 0.02], + "humerus": [0, 0, 0.036], + "shoulder": [0, 0, 0], + "base": [0, 0, 0.02], + }, + "moss": { + "gripper": [0.246, 0.013, 0.111], + "wrist": [0.245, 0.002, 0.064], + "forearm": [0.122, 0, 0.064], + "humerus": [0.001, 0.001, 0.063], + "shoulder": [0, 0, 0], + "base": [0, 0, 0.02], + }, + "so_old_calibration": { + "gripper": [0.320, 0, 0.050], + "wrist": [0.278, 0, 0.050], + "forearm": [0.143, 0, 0.044], + "humerus": [0.031, 0, 0.072], + "shoulder": [0, 0, 0], + "base": [0, 0, 0.02], + }, + "so_new_calibration": { + "gripper": [0.33, 0.0, 0.285], + "wrist": [0.30, 0.0, 0.267], + "forearm": [0.25, 0.0, 0.266], + "humerus": [0.06, 0.0, 0.264], + "shoulder": [0.0, 0.0, 0.238], + "base": [0.0, 0.0, 0.12], + }, + } + + def __init__(self, robot_type: str = "so100"): + """Initialize kinematics for the specified robot type. + + Args: + robot_type: String specifying the robot model ("koch", "so100", or "moss") + """ + if robot_type not in self.ROBOT_MEASUREMENTS: + raise ValueError( + f"Unknown robot type: {robot_type}. Available types: {list(self.ROBOT_MEASUREMENTS.keys())}" + ) + + self.robot_type = robot_type + self.measurements = self.ROBOT_MEASUREMENTS[robot_type] + + # Initialize all transformation matrices and screw axes + self._setup_transforms() + + def _create_translation_matrix( + self, x: float = 0.0, y: float = 0.0, z: float = 0.0 + ) -> NDArray[np.float32]: + """Create a 4x4 translation matrix.""" + return np.array([[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]]) + + def _setup_transforms(self): + """Setup all transformation matrices and screw axes for the robot.""" + # Set up rotation matrices (constant across robot types) + + # Gripper orientation + self.gripper_X0 = np.array( + [ + [1, 0, 0, 0], + [0, 0, 1, 0], + [0, -1, 0, 0], + [0, 0, 0, 1], + ], + dtype=np.float32, + ) + + # Wrist orientation + self.wrist_X0 = np.array( + [ + [0, -1, 0, 0], + [1, 0, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ], + dtype=np.float32, + ) + + # Base orientation + self.base_X0 = np.array( + [ + [0, 0, 1, 0], + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 0, 1], + ], + dtype=np.float32, + ) + + # Gripper + # Screw axis of gripper frame wrt base frame + self.S_BG = np.array( + [ + 1, + 0, + 0, + 0, + self.measurements["gripper"][2], + -self.measurements["gripper"][1], + ], + dtype=np.float32, + ) + + # Gripper origin to centroid transform + self.X_GoGc = self._create_translation_matrix(x=0.07) + + # Gripper origin to tip transform + self.X_GoGt = self._create_translation_matrix(x=0.12) + + # 0-position gripper frame pose wrt base + self.X_BoGo = self._create_translation_matrix( + x=self.measurements["gripper"][0], + y=self.measurements["gripper"][1], + z=self.measurements["gripper"][2], + ) + + # Wrist + # Screw axis of wrist frame wrt base frame + self.S_BR = np.array( + [0, 1, 0, -self.measurements["wrist"][2], 0, self.measurements["wrist"][0]], dtype=np.float32 + ) + + # 0-position origin to centroid transform + self.X_RoRc = self._create_translation_matrix(x=0.0035, y=-0.002) + + # 0-position wrist frame pose wrt base + self.X_BR = self._create_translation_matrix( + x=self.measurements["wrist"][0], + y=self.measurements["wrist"][1], + z=self.measurements["wrist"][2], + ) + + # Forearm + # Screw axis of forearm frame wrt base frame + self.S_BF = np.array( + [ + 0, + 1, + 0, + -self.measurements["forearm"][2], + 0, + self.measurements["forearm"][0], + ], + dtype=np.float32, + ) + + # Forearm origin + centroid transform + self.X_ForearmFc = self._create_translation_matrix(x=0.036) + + # 0-position forearm frame pose wrt base + self.X_BF = self._create_translation_matrix( + x=self.measurements["forearm"][0], + y=self.measurements["forearm"][1], + z=self.measurements["forearm"][2], + ) + + # Humerus + # Screw axis of humerus frame wrt base frame + self.S_BH = np.array( + [ + 0, + -1, + 0, + self.measurements["humerus"][2], + 0, + -self.measurements["humerus"][0], + ], + dtype=np.float32, + ) + + # Humerus origin to centroid transform + self.X_HoHc = self._create_translation_matrix(x=0.0475) + + # 0-position humerus frame pose wrt base + self.X_BH = self._create_translation_matrix( + x=self.measurements["humerus"][0], + y=self.measurements["humerus"][1], + z=self.measurements["humerus"][2], + ) + + # Shoulder + # Screw axis of shoulder frame wrt Base frame + self.S_BS = np.array([0, 0, -1, 0, 0, 0], dtype=np.float32) + + # Shoulder origin to centroid transform + self.X_SoSc = self._create_translation_matrix(x=-0.017, z=0.0235) + + # 0-position shoulder frame pose wrt base + self.X_BS = self._create_translation_matrix( + x=self.measurements["shoulder"][0], + y=self.measurements["shoulder"][1], + z=self.measurements["shoulder"][2], + ) + + # Base + # Base origin to centroid transform + self.X_BoBc = self._create_translation_matrix(y=0.015) + + # World to base transform + self.X_WoBo = self._create_translation_matrix( + x=self.measurements["base"][0], + y=self.measurements["base"][1], + z=self.measurements["base"][2], + ) + + # Pre-compute gripper post-multiplication matrix + self._fk_gripper_post = self.X_GoGc @ self.X_BoGo @ self.gripper_X0 + + def forward_kinematics( + self, + robot_pos_deg: NDArray[np.float32], + frame: str = "gripper_tip", + ) -> NDArray[np.float32]: + """Generic forward kinematics. + + Args: + robot_pos_deg: Joint positions in degrees. Can be ``None`` when + computing the *base* frame as it does not depend on joint + angles. + frame: Target frame. One of + ``{"base", "shoulder", "humerus", "forearm", "wrist", "gripper", "gripper_tip"}``. + + Returns + ------- + NDArray[np.float32] + 4×4 homogeneous transformation matrix of the requested frame + expressed in the world coordinate system. + """ + frame = frame.lower() + if frame not in { + "base", + "shoulder", + "humerus", + "forearm", + "wrist", + "gripper", + "gripper_tip", + }: + raise ValueError( + f"Unknown frame '{frame}'. Valid options are base, shoulder, humerus, forearm, wrist, gripper, gripper_tip." + ) + + # Base frame does not rely on joint angles. + if frame == "base": + return self.X_WoBo @ self.X_BoBc @ self.base_X0 + + robot_pos_rad = robot_pos_deg / 180 * np.pi + + # Extract joint angles (note the sign convention for shoulder lift). + theta_shoulder_pan = robot_pos_rad[0] + theta_shoulder_lift = -robot_pos_rad[1] + theta_elbow_flex = robot_pos_rad[2] + theta_wrist_flex = robot_pos_rad[3] + theta_wrist_roll = robot_pos_rad[4] + + # Start with the world-to-base transform; incrementally add successive links. + transformation_matrix = self.X_WoBo @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan) + if frame == "shoulder": + return transformation_matrix @ self.X_SoSc @ self.X_BS + + transformation_matrix = transformation_matrix @ screw_axis_to_transform( + self.S_BH, theta_shoulder_lift + ) + if frame == "humerus": + return transformation_matrix @ self.X_HoHc @ self.X_BH + + transformation_matrix = transformation_matrix @ screw_axis_to_transform(self.S_BF, theta_elbow_flex) + if frame == "forearm": + return transformation_matrix @ self.X_ForearmFc @ self.X_BF + + transformation_matrix = transformation_matrix @ screw_axis_to_transform(self.S_BR, theta_wrist_flex) + if frame == "wrist": + return transformation_matrix @ self.X_RoRc @ self.X_BR @ self.wrist_X0 + + transformation_matrix = transformation_matrix @ screw_axis_to_transform(self.S_BG, theta_wrist_roll) + if frame == "gripper": + return transformation_matrix @ self._fk_gripper_post + else: # frame == "gripper_tip" + return transformation_matrix @ self.X_GoGt @ self.X_BoGo @ self.gripper_X0 + + def compute_jacobian( + self, robot_pos_deg: NDArray[np.float32], frame: str = "gripper_tip" + ) -> NDArray[np.float32]: + """Finite differences to compute the Jacobian. + J(i, j) represents how the ith component of the end-effector's velocity changes wrt a small change + in the jth joint's velocity. + + Args: + robot_pos_deg: Current joint positions in degrees + fk_func: Forward kinematics function to use (defaults to fk_gripper) + """ + + eps = 1e-8 + jac = np.zeros(shape=(6, 5)) + delta = np.zeros(len(robot_pos_deg[:-1]), dtype=np.float64) + for el_ix in range(len(robot_pos_deg[:-1])): + delta *= 0 + delta[el_ix] = eps / 2 + sdot = ( + pose_difference_se3( + self.forward_kinematics(robot_pos_deg[:-1] + delta, frame), + self.forward_kinematics(robot_pos_deg[:-1] - delta, frame), + ) + / eps + ) + jac[:, el_ix] = sdot + return jac + + def compute_positional_jacobian( + self, robot_pos_deg: NDArray[np.float32], frame: str = "gripper_tip" + ) -> NDArray[np.float32]: + """Finite differences to compute the positional Jacobian. + J(i, j) represents how the ith component of the end-effector's position changes wrt a small change + in the jth joint's velocity. + + Args: + robot_pos_deg: Current joint positions in degrees + fk_func: Forward kinematics function to use (defaults to fk_gripper) + """ + eps = 1e-8 + jac = np.zeros(shape=(3, 5)) + delta = np.zeros(len(robot_pos_deg[:-1]), dtype=np.float64) + for el_ix in range(len(robot_pos_deg[:-1])): + delta *= 0 + delta[el_ix] = eps / 2 + sdot = ( + self.forward_kinematics(robot_pos_deg[:-1] + delta, frame)[:3, 3] + - self.forward_kinematics(robot_pos_deg[:-1] - delta, frame)[:3, 3] + ) / eps + jac[:, el_ix] = sdot + return jac + + def ik( + self, + current_joint_pos: NDArray[np.float32], + desired_ee_pose: NDArray[np.float32], + position_only: bool = True, + frame: str = "gripper_tip", + max_iterations: int = 5, + learning_rate: float = 1, + ) -> NDArray[np.float32]: + """Inverse kinematics using gradient descent. + + Args: + current_joint_state: Initial joint positions in degrees + desired_ee_pose: Target end-effector pose as a 4x4 transformation matrix + position_only: If True, only match end-effector position, not orientation + frame: Target frame. One of + ``{"base", "shoulder", "humerus", "forearm", "wrist", "gripper", "gripper_tip"}``. + max_iterations: Maximum number of iterations to run + learning_rate: Learning rate for gradient descent + + Returns: + Joint positions in degrees that achieve the desired end-effector pose + """ + # Do gradient descent. + current_joint_state = current_joint_pos.copy() + for _ in range(max_iterations): + current_ee_pose = self.forward_kinematics(current_joint_state, frame) + if not position_only: + error = se3_error(desired_ee_pose, current_ee_pose) + jac = self.compute_jacobian(current_joint_state, frame) + else: + error = desired_ee_pose[:3, 3] - current_ee_pose[:3, 3] + jac = self.compute_positional_jacobian(current_joint_state, frame) + delta_angles = np.linalg.pinv(jac) @ error + current_joint_state[:-1] += learning_rate * delta_angles + + if np.linalg.norm(error) < 5e-3: + return current_joint_state + return current_joint_state diff --git a/lerobot/common/motors/__init__.py b/lerobot/common/motors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..75311109d101e690f5d13afe050b24ec8e4ce69e --- /dev/null +++ b/lerobot/common/motors/__init__.py @@ -0,0 +1 @@ +from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus diff --git a/lerobot/common/motors/dynamixel/__init__.py b/lerobot/common/motors/dynamixel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..31cf82a6d661d7392bac3a9bb2d9c92b30749b44 --- /dev/null +++ b/lerobot/common/motors/dynamixel/__init__.py @@ -0,0 +1,2 @@ +from .dynamixel import DriveMode, DynamixelMotorsBus, OperatingMode, TorqueMode +from .tables import * diff --git a/lerobot/common/motors/dynamixel/dynamixel.py b/lerobot/common/motors/dynamixel/dynamixel.py new file mode 100644 index 0000000000000000000000000000000000000000..fc3c1145b4072087773b846f9b1c91fdb2f9d712 --- /dev/null +++ b/lerobot/common/motors/dynamixel/dynamixel.py @@ -0,0 +1,263 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# TODO(aliberts): Should we implement FastSyncRead/Write? +# https://github.com/ROBOTIS-GIT/DynamixelSDK/pull/643 +# https://github.com/ROBOTIS-GIT/DynamixelSDK/releases/tag/3.8.2 +# https://emanual.robotis.com/docs/en/dxl/protocol2/#fast-sync-read-0x8a +# -> Need to check compatibility across models + +import logging +from copy import deepcopy +from enum import Enum + +from lerobot.common.utils.encoding_utils import decode_twos_complement, encode_twos_complement + +from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address +from .tables import ( + AVAILABLE_BAUDRATES, + MODEL_BAUDRATE_TABLE, + MODEL_CONTROL_TABLE, + MODEL_ENCODING_TABLE, + MODEL_NUMBER_TABLE, + MODEL_RESOLUTION, +) + +PROTOCOL_VERSION = 2.0 +DEFAULT_BAUDRATE = 1_000_000 +DEFAULT_TIMEOUT_MS = 1000 + +NORMALIZED_DATA = ["Goal_Position", "Present_Position"] + +logger = logging.getLogger(__name__) + + +class OperatingMode(Enum): + # DYNAMIXEL only controls current(torque) regardless of speed and position. This mode is ideal for a + # gripper or a system that only uses current(torque) control or a system that has additional + # velocity/position controllers. + CURRENT = 0 + + # This mode controls velocity. This mode is identical to the Wheel Mode(endless) from existing DYNAMIXEL. + # This mode is ideal for wheel-type robots. + VELOCITY = 1 + + # This mode controls position. This mode is identical to the Joint Mode from existing DYNAMIXEL. Operating + # position range is limited by the Max Position Limit(48) and the Min Position Limit(52). This mode is + # ideal for articulated robots that each joint rotates less than 360 degrees. + POSITION = 3 + + # This mode controls position. This mode is identical to the Multi-turn Position Control from existing + # DYNAMIXEL. 512 turns are supported(-256[rev] ~ 256[rev]). This mode is ideal for multi-turn wrists or + # conveyer systems or a system that requires an additional reduction gear. Note that Max Position + # Limit(48), Min Position Limit(52) are not used on Extended Position Control Mode. + EXTENDED_POSITION = 4 + + # This mode controls both position and current(torque). Up to 512 turns are supported (-256[rev] ~ + # 256[rev]). This mode is ideal for a system that requires both position and current control such as + # articulated robots or grippers. + CURRENT_POSITION = 5 + + # This mode directly controls PWM output. (Voltage Control Mode) + PWM = 16 + + +class DriveMode(Enum): + NON_INVERTED = 0 + INVERTED = 1 + + +class TorqueMode(Enum): + ENABLED = 1 + DISABLED = 0 + + +def _split_into_byte_chunks(value: int, length: int) -> list[int]: + import dynamixel_sdk as dxl + + if length == 1: + data = [value] + elif length == 2: + data = [dxl.DXL_LOBYTE(value), dxl.DXL_HIBYTE(value)] + elif length == 4: + data = [ + dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)), + dxl.DXL_HIBYTE(dxl.DXL_LOWORD(value)), + dxl.DXL_LOBYTE(dxl.DXL_HIWORD(value)), + dxl.DXL_HIBYTE(dxl.DXL_HIWORD(value)), + ] + return data + + +class DynamixelMotorsBus(MotorsBus): + """ + The Dynamixel implementation for a MotorsBus. It relies on the python dynamixel sdk to communicate with + the motors. For more info, see the Dynamixel SDK Documentation: + https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20 + """ + + apply_drive_mode = False + available_baudrates = deepcopy(AVAILABLE_BAUDRATES) + default_baudrate = DEFAULT_BAUDRATE + default_timeout = DEFAULT_TIMEOUT_MS + model_baudrate_table = deepcopy(MODEL_BAUDRATE_TABLE) + model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE) + model_encoding_table = deepcopy(MODEL_ENCODING_TABLE) + model_number_table = deepcopy(MODEL_NUMBER_TABLE) + model_resolution_table = deepcopy(MODEL_RESOLUTION) + normalized_data = deepcopy(NORMALIZED_DATA) + + def __init__( + self, + port: str, + motors: dict[str, Motor], + calibration: dict[str, MotorCalibration] | None = None, + ): + super().__init__(port, motors, calibration) + import dynamixel_sdk as dxl + + self.port_handler = dxl.PortHandler(self.port) + self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION) + self.sync_reader = dxl.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0) + self.sync_writer = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0) + self._comm_success = dxl.COMM_SUCCESS + self._no_error = 0x00 + + def _assert_protocol_is_compatible(self, instruction_name: str) -> None: + pass + + def _handshake(self) -> None: + self._assert_motors_exist() + + def _find_single_motor(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]: + model = self.motors[motor].model + search_baudrates = ( + [initial_baudrate] if initial_baudrate is not None else self.model_baudrate_table[model] + ) + + for baudrate in search_baudrates: + self.set_baudrate(baudrate) + id_model = self.broadcast_ping() + if id_model: + found_id, found_model = next(iter(id_model.items())) + expected_model_nb = self.model_number_table[model] + if found_model != expected_model_nb: + raise RuntimeError( + f"Found one motor on {baudrate=} with id={found_id} but it has a " + f"model number '{found_model}' different than the one expected: '{expected_model_nb}'. " + f"Make sure you are connected only connected to the '{motor}' motor (model '{model}')." + ) + return baudrate, found_id + + raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.") + + def configure_motors(self) -> None: + # By default, Dynamixel motors have a 500µs delay response time (corresponding to a value of 250 on + # the 'Return_Delay_Time' address). We ensure this is reduced to the minimum of 2µs (value of 0). + for motor in self.motors: + self.write("Return_Delay_Time", motor, 0) + + @property + def is_calibrated(self) -> bool: + return self.calibration == self.read_calibration() + + def read_calibration(self) -> dict[str, MotorCalibration]: + offsets = self.sync_read("Homing_Offset", normalize=False) + mins = self.sync_read("Min_Position_Limit", normalize=False) + maxes = self.sync_read("Max_Position_Limit", normalize=False) + drive_modes = self.sync_read("Drive_Mode", normalize=False) + + calibration = {} + for motor, m in self.motors.items(): + calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=drive_modes[motor], + homing_offset=offsets[motor], + range_min=mins[motor], + range_max=maxes[motor], + ) + + return calibration + + def write_calibration(self, calibration_dict: dict[str, MotorCalibration]) -> None: + for motor, calibration in calibration_dict.items(): + self.write("Homing_Offset", motor, calibration.homing_offset) + self.write("Min_Position_Limit", motor, calibration.range_min) + self.write("Max_Position_Limit", motor, calibration.range_max) + + self.calibration = calibration_dict + + def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None: + for motor in self._get_motors_list(motors): + self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry) + + def _disable_torque(self, motor_id: int, model: str, num_retry: int = 0) -> None: + addr, length = get_address(self.model_ctrl_table, model, "Torque_Enable") + self._write(addr, length, motor_id, TorqueMode.DISABLED.value, num_retry=num_retry) + + def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None: + for motor in self._get_motors_list(motors): + self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry) + + def _encode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]: + for id_ in ids_values: + model = self._id_to_model(id_) + encoding_table = self.model_encoding_table.get(model) + if encoding_table and data_name in encoding_table: + n_bytes = encoding_table[data_name] + ids_values[id_] = encode_twos_complement(ids_values[id_], n_bytes) + + return ids_values + + def _decode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]: + for id_ in ids_values: + model = self._id_to_model(id_) + encoding_table = self.model_encoding_table.get(model) + if encoding_table and data_name in encoding_table: + n_bytes = encoding_table[data_name] + ids_values[id_] = decode_twos_complement(ids_values[id_], n_bytes) + + return ids_values + + def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]: + """ + On Dynamixel Motors: + Present_Position = Actual_Position + Homing_Offset + """ + half_turn_homings = {} + for motor, pos in positions.items(): + model = self._get_motor_model(motor) + max_res = self.model_resolution_table[model] - 1 + half_turn_homings[motor] = int(max_res / 2) - pos + + return half_turn_homings + + def _split_into_byte_chunks(self, value: int, length: int) -> list[int]: + return _split_into_byte_chunks(value, length) + + def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None: + for n_try in range(1 + num_retry): + data_list, comm = self.packet_handler.broadcastPing(self.port_handler) + if self._is_comm_success(comm): + break + logger.debug(f"Broadcast ping failed on port '{self.port}' ({n_try=})") + logger.debug(self.packet_handler.getTxRxResult(comm)) + + if not self._is_comm_success(comm): + if raise_on_error: + raise ConnectionError(self.packet_handler.getTxRxResult(comm)) + + return + + return {id_: data[0] for id_, data in data_list.items()} diff --git a/lerobot/common/motors/dynamixel/tables.py b/lerobot/common/motors/dynamixel/tables.py new file mode 100644 index 0000000000000000000000000000000000000000..88a9ac648ab361f7b31386860460c4b69e144cb7 --- /dev/null +++ b/lerobot/common/motors/dynamixel/tables.py @@ -0,0 +1,197 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# TODO(Steven): Consider doing the following: +# from enum import Enum +# class MyControlTableKey(Enum): +# ID = "ID" +# GOAL_SPEED = "Goal_Speed" +# ... +# +# MY_CONTROL_TABLE ={ +# MyControlTableKey.ID.value: (5,1) +# MyControlTableKey.GOAL_SPEED.value: (46, 2) +# ... +# } +# This allows me do to: +# bus.write(MyControlTableKey.GOAL_SPEED, ...) +# Instead of: +# bus.write("Goal_Speed", ...) +# This is important for two reasons: +# 1. The linter will tell me if I'm trying to use an invalid key, instead of me realizing when I get the RunTimeError +# 2. We can change the value of the MyControlTableKey enums without impacting the client code + + +# {data_name: (address, size_byte)} +# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table +X_SERIES_CONTROL_TABLE = { + "Model_Number": (0, 2), + "Model_Information": (2, 4), + "Firmware_Version": (6, 1), + "ID": (7, 1), + "Baud_Rate": (8, 1), + "Return_Delay_Time": (9, 1), + "Drive_Mode": (10, 1), + "Operating_Mode": (11, 1), + "Secondary_ID": (12, 1), + "Protocol_Type": (13, 1), + "Homing_Offset": (20, 4), + "Moving_Threshold": (24, 4), + "Temperature_Limit": (31, 1), + "Max_Voltage_Limit": (32, 2), + "Min_Voltage_Limit": (34, 2), + "PWM_Limit": (36, 2), + "Current_Limit": (38, 2), + "Acceleration_Limit": (40, 4), + "Velocity_Limit": (44, 4), + "Max_Position_Limit": (48, 4), + "Min_Position_Limit": (52, 4), + "Shutdown": (63, 1), + "Torque_Enable": (64, 1), + "LED": (65, 1), + "Status_Return_Level": (68, 1), + "Registered_Instruction": (69, 1), + "Hardware_Error_Status": (70, 1), + "Velocity_I_Gain": (76, 2), + "Velocity_P_Gain": (78, 2), + "Position_D_Gain": (80, 2), + "Position_I_Gain": (82, 2), + "Position_P_Gain": (84, 2), + "Feedforward_2nd_Gain": (88, 2), + "Feedforward_1st_Gain": (90, 2), + "Bus_Watchdog": (98, 1), + "Goal_PWM": (100, 2), + "Goal_Current": (102, 2), + "Goal_Velocity": (104, 4), + "Profile_Acceleration": (108, 4), + "Profile_Velocity": (112, 4), + "Goal_Position": (116, 4), + "Realtime_Tick": (120, 2), + "Moving": (122, 1), + "Moving_Status": (123, 1), + "Present_PWM": (124, 2), + "Present_Current": (126, 2), + "Present_Velocity": (128, 4), + "Present_Position": (132, 4), + "Velocity_Trajectory": (136, 4), + "Position_Trajectory": (140, 4), + "Present_Input_Voltage": (144, 2), + "Present_Temperature": (146, 1), +} + +# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#baud-rate8 +X_SERIES_BAUDRATE_TABLE = { + 9_600: 0, + 57_600: 1, + 115_200: 2, + 1_000_000: 3, + 2_000_000: 4, + 3_000_000: 5, + 4_000_000: 6, +} + +# {data_name: size_byte} +X_SERIES_ENCODINGS_TABLE = { + "Homing_Offset": X_SERIES_CONTROL_TABLE["Homing_Offset"][1], + "Goal_PWM": X_SERIES_CONTROL_TABLE["Goal_PWM"][1], + "Goal_Current": X_SERIES_CONTROL_TABLE["Goal_Current"][1], + "Goal_Velocity": X_SERIES_CONTROL_TABLE["Goal_Velocity"][1], + "Present_PWM": X_SERIES_CONTROL_TABLE["Present_PWM"][1], + "Present_Current": X_SERIES_CONTROL_TABLE["Present_Current"][1], + "Present_Velocity": X_SERIES_CONTROL_TABLE["Present_Velocity"][1], +} + +MODEL_ENCODING_TABLE = { + "x_series": X_SERIES_ENCODINGS_TABLE, + "xl330-m077": X_SERIES_ENCODINGS_TABLE, + "xl330-m288": X_SERIES_ENCODINGS_TABLE, + "xl430-w250": X_SERIES_ENCODINGS_TABLE, + "xm430-w350": X_SERIES_ENCODINGS_TABLE, + "xm540-w270": X_SERIES_ENCODINGS_TABLE, + "xc430-w150": X_SERIES_ENCODINGS_TABLE, +} + +# {model: model_resolution} +# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#specifications +MODEL_RESOLUTION = { + "x_series": 4096, + "xl330-m077": 4096, + "xl330-m288": 4096, + "xl430-w250": 4096, + "xm430-w350": 4096, + "xm540-w270": 4096, + "xc430-w150": 4096, +} + +# {model: model_number} +# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table-of-eeprom-area +MODEL_NUMBER_TABLE = { + "xl330-m077": 1190, + "xl330-m288": 1200, + "xl430-w250": 1060, + "xm430-w350": 1020, + "xm540-w270": 1120, + "xc430-w150": 1070, +} + +# {model: available_operating_modes} +# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#operating-mode11 +MODEL_OPERATING_MODES = { + "xl330-m077": [0, 1, 3, 4, 5, 16], + "xl330-m288": [0, 1, 3, 4, 5, 16], + "xl430-w250": [1, 3, 4, 16], + "xm430-w350": [0, 1, 3, 4, 5, 16], + "xm540-w270": [0, 1, 3, 4, 5, 16], + "xc430-w150": [1, 3, 4, 16], +} + +MODEL_CONTROL_TABLE = { + "x_series": X_SERIES_CONTROL_TABLE, + "xl330-m077": X_SERIES_CONTROL_TABLE, + "xl330-m288": X_SERIES_CONTROL_TABLE, + "xl430-w250": X_SERIES_CONTROL_TABLE, + "xm430-w350": X_SERIES_CONTROL_TABLE, + "xm540-w270": X_SERIES_CONTROL_TABLE, + "xc430-w150": X_SERIES_CONTROL_TABLE, +} + +MODEL_BAUDRATE_TABLE = { + "x_series": X_SERIES_BAUDRATE_TABLE, + "xl330-m077": X_SERIES_BAUDRATE_TABLE, + "xl330-m288": X_SERIES_BAUDRATE_TABLE, + "xl430-w250": X_SERIES_BAUDRATE_TABLE, + "xm430-w350": X_SERIES_BAUDRATE_TABLE, + "xm540-w270": X_SERIES_BAUDRATE_TABLE, + "xc430-w150": X_SERIES_BAUDRATE_TABLE, +} + +AVAILABLE_BAUDRATES = [ + 9_600, + 19_200, + 38_400, + 57_600, + 115_200, + 230_400, + 460_800, + 500_000, + 576_000, + 921_600, + 1_000_000, + 1_152_000, + 2_000_000, + 2_500_000, + 3_000_000, + 3_500_000, + 4_000_000, +] diff --git a/lerobot/common/motors/feetech/__init__.py b/lerobot/common/motors/feetech/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b606ee86ef6777ae5eb4d45750f21d113f15cf1e --- /dev/null +++ b/lerobot/common/motors/feetech/__init__.py @@ -0,0 +1,2 @@ +from .feetech import DriveMode, FeetechMotorsBus, OperatingMode, TorqueMode +from .tables import * diff --git a/lerobot/common/motors/feetech/feetech.py b/lerobot/common/motors/feetech/feetech.py new file mode 100644 index 0000000000000000000000000000000000000000..d4d930fd109560648562c8c58a0d2a0d69544abe --- /dev/null +++ b/lerobot/common/motors/feetech/feetech.py @@ -0,0 +1,454 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from copy import deepcopy +from enum import Enum +from pprint import pformat + +from lerobot.common.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude + +from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address +from .tables import ( + FIRMWARE_MAJOR_VERSION, + FIRMWARE_MINOR_VERSION, + MODEL_BAUDRATE_TABLE, + MODEL_CONTROL_TABLE, + MODEL_ENCODING_TABLE, + MODEL_NUMBER, + MODEL_NUMBER_TABLE, + MODEL_PROTOCOL, + MODEL_RESOLUTION, + SCAN_BAUDRATES, +) + +DEFAULT_PROTOCOL_VERSION = 0 +DEFAULT_BAUDRATE = 1_000_000 +DEFAULT_TIMEOUT_MS = 1000 + +NORMALIZED_DATA = ["Goal_Position", "Present_Position"] + +logger = logging.getLogger(__name__) + + +class OperatingMode(Enum): + # position servo mode + POSITION = 0 + # The motor is in constant speed mode, which is controlled by parameter 0x2e, and the highest bit 15 is + # the direction bit + VELOCITY = 1 + # PWM open-loop speed regulation mode, with parameter 0x2c running time parameter control, bit11 as + # direction bit + PWM = 2 + # In step servo mode, the number of step progress is represented by parameter 0x2a, and the highest bit 15 + # is the direction bit + STEP = 3 + + +class DriveMode(Enum): + NON_INVERTED = 0 + INVERTED = 1 + + +class TorqueMode(Enum): + ENABLED = 1 + DISABLED = 0 + + +def _split_into_byte_chunks(value: int, length: int) -> list[int]: + import scservo_sdk as scs + + if length == 1: + data = [value] + elif length == 2: + data = [scs.SCS_LOBYTE(value), scs.SCS_HIBYTE(value)] + elif length == 4: + data = [ + scs.SCS_LOBYTE(scs.SCS_LOWORD(value)), + scs.SCS_HIBYTE(scs.SCS_LOWORD(value)), + scs.SCS_LOBYTE(scs.SCS_HIWORD(value)), + scs.SCS_HIBYTE(scs.SCS_HIWORD(value)), + ] + return data + + +def patch_setPacketTimeout(self, packet_length): # noqa: N802 + """ + HACK: This patches the PortHandler behavior to set the correct packet timeouts. + + It fixes https://gitee.com/ftservo/SCServoSDK/issues/IBY2S6 + The bug is fixed on the official Feetech SDK repo (https://gitee.com/ftservo/FTServo_Python) + but because that version is not published on PyPI, we rely on the (unofficial) on that is, which needs + patching. + """ + self.packet_start_time = self.getCurrentTime() + self.packet_timeout = (self.tx_time_per_byte * packet_length) + (self.tx_time_per_byte * 3.0) + 50 + + +class FeetechMotorsBus(MotorsBus): + """ + The FeetechMotorsBus class allows to efficiently read and write to the attached motors. It relies on the + python feetech sdk to communicate with the motors, which is itself based on the dynamixel sdk. + """ + + apply_drive_mode = True + available_baudrates = deepcopy(SCAN_BAUDRATES) + default_baudrate = DEFAULT_BAUDRATE + default_timeout = DEFAULT_TIMEOUT_MS + model_baudrate_table = deepcopy(MODEL_BAUDRATE_TABLE) + model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE) + model_encoding_table = deepcopy(MODEL_ENCODING_TABLE) + model_number_table = deepcopy(MODEL_NUMBER_TABLE) + model_resolution_table = deepcopy(MODEL_RESOLUTION) + normalized_data = deepcopy(NORMALIZED_DATA) + + def __init__( + self, + port: str, + motors: dict[str, Motor], + calibration: dict[str, MotorCalibration] | None = None, + protocol_version: int = DEFAULT_PROTOCOL_VERSION, + ): + super().__init__(port, motors, calibration) + self.protocol_version = protocol_version + self._assert_same_protocol() + import scservo_sdk as scs + + self.port_handler = scs.PortHandler(self.port) + # HACK: monkeypatch + self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__( + self.port_handler, scs.PortHandler + ) + self.packet_handler = scs.PacketHandler(protocol_version) + self.sync_reader = scs.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0) + self.sync_writer = scs.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0) + self._comm_success = scs.COMM_SUCCESS + self._no_error = 0x00 + + if any(MODEL_PROTOCOL[model] != self.protocol_version for model in self.models): + raise ValueError(f"Some motors are incompatible with protocol_version={self.protocol_version}") + + def _assert_same_protocol(self) -> None: + if any(MODEL_PROTOCOL[model] != self.protocol_version for model in self.models): + raise RuntimeError("Some motors use an incompatible protocol.") + + def _assert_protocol_is_compatible(self, instruction_name: str) -> None: + if instruction_name == "sync_read" and self.protocol_version == 1: + raise NotImplementedError( + "'Sync Read' is not available with Feetech motors using Protocol 1. Use 'Read' sequentially instead." + ) + if instruction_name == "broadcast_ping" and self.protocol_version == 1: + raise NotImplementedError( + "'Broadcast Ping' is not available with Feetech motors using Protocol 1. Use 'Ping' sequentially instead." + ) + + def _assert_same_firmware(self) -> None: + firmware_versions = self._read_firmware_version(self.ids, raise_on_error=True) + if len(set(firmware_versions.values())) != 1: + raise RuntimeError( + "Some Motors use different firmware versions:" + f"\n{pformat(firmware_versions)}\n" + "Update their firmware first using Feetech's software. " + "Visit https://www.feetechrc.com/software." + ) + + def _handshake(self) -> None: + self._assert_motors_exist() + self._assert_same_firmware() + + def _find_single_motor(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]: + if self.protocol_version == 0: + return self._find_single_motor_p0(motor, initial_baudrate) + else: + return self._find_single_motor_p1(motor, initial_baudrate) + + def _find_single_motor_p0(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]: + model = self.motors[motor].model + search_baudrates = ( + [initial_baudrate] if initial_baudrate is not None else self.model_baudrate_table[model] + ) + expected_model_nb = self.model_number_table[model] + + for baudrate in search_baudrates: + self.set_baudrate(baudrate) + id_model = self.broadcast_ping() + if id_model: + found_id, found_model = next(iter(id_model.items())) + if found_model != expected_model_nb: + raise RuntimeError( + f"Found one motor on {baudrate=} with id={found_id} but it has a " + f"model number '{found_model}' different than the one expected: '{expected_model_nb}'. " + f"Make sure you are connected only connected to the '{motor}' motor (model '{model}')." + ) + return baudrate, found_id + + raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.") + + def _find_single_motor_p1(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]: + import scservo_sdk as scs + + model = self.motors[motor].model + search_baudrates = ( + [initial_baudrate] if initial_baudrate is not None else self.model_baudrate_table[model] + ) + expected_model_nb = self.model_number_table[model] + + for baudrate in search_baudrates: + self.set_baudrate(baudrate) + for id_ in range(scs.MAX_ID + 1): + found_model = self.ping(id_) + if found_model is not None: + if found_model != expected_model_nb: + raise RuntimeError( + f"Found one motor on {baudrate=} with id={id_} but it has a " + f"model number '{found_model}' different than the one expected: '{expected_model_nb}'. " + f"Make sure you are connected only connected to the '{motor}' motor (model '{model}')." + ) + return baudrate, id_ + + raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.") + + def configure_motors(self) -> None: + for motor in self.motors: + # By default, Feetech motors have a 500µs delay response time (corresponding to a value of 250 on + # the 'Return_Delay_Time' address). We ensure this is reduced to the minimum of 2µs (value of 0). + self.write("Return_Delay_Time", motor, 0) + # Set 'Maximum_Acceleration' to 254 to speedup acceleration and deceleration of the motors. + # Note: this address is not in the official STS3215 Memory Table + self.write("Maximum_Acceleration", motor, 254) + self.write("Acceleration", motor, 254) + + @property + def is_calibrated(self) -> bool: + motors_calibration = self.read_calibration() + if set(motors_calibration) != set(self.calibration): + return False + + same_ranges = all( + self.calibration[motor].range_min == cal.range_min + and self.calibration[motor].range_max == cal.range_max + for motor, cal in motors_calibration.items() + ) + if self.protocol_version == 1: + return same_ranges + + same_offsets = all( + self.calibration[motor].homing_offset == cal.homing_offset + for motor, cal in motors_calibration.items() + ) + return same_ranges and same_offsets + + def read_calibration(self) -> dict[str, MotorCalibration]: + offsets, mins, maxes = {}, {}, {} + for motor in self.motors: + mins[motor] = self.read("Min_Position_Limit", motor, normalize=False) + maxes[motor] = self.read("Max_Position_Limit", motor, normalize=False) + offsets[motor] = ( + self.read("Homing_Offset", motor, normalize=False) if self.protocol_version == 0 else 0 + ) + + calibration = {} + for motor, m in self.motors.items(): + calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=offsets[motor], + range_min=mins[motor], + range_max=maxes[motor], + ) + + return calibration + + def write_calibration(self, calibration_dict: dict[str, MotorCalibration]) -> None: + for motor, calibration in calibration_dict.items(): + if self.protocol_version == 0: + self.write("Homing_Offset", motor, calibration.homing_offset) + self.write("Min_Position_Limit", motor, calibration.range_min) + self.write("Max_Position_Limit", motor, calibration.range_max) + + self.calibration = calibration_dict + + def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]: + """ + On Feetech Motors: + Present_Position = Actual_Position - Homing_Offset + """ + half_turn_homings = {} + for motor, pos in positions.items(): + model = self._get_motor_model(motor) + max_res = self.model_resolution_table[model] - 1 + half_turn_homings[motor] = pos - int(max_res / 2) + + return half_turn_homings + + def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None: + for motor in self._get_motors_list(motors): + self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry) + self.write("Lock", motor, 0, num_retry=num_retry) + + def _disable_torque(self, motor_id: int, model: str, num_retry: int = 0) -> None: + addr, length = get_address(self.model_ctrl_table, model, "Torque_Enable") + self._write(addr, length, motor_id, TorqueMode.DISABLED.value, num_retry=num_retry) + addr, length = get_address(self.model_ctrl_table, model, "Lock") + self._write(addr, length, motor_id, 0, num_retry=num_retry) + + def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None: + for motor in self._get_motors_list(motors): + self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry) + self.write("Lock", motor, 1, num_retry=num_retry) + + def _encode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]: + for id_ in ids_values: + model = self._id_to_model(id_) + encoding_table = self.model_encoding_table.get(model) + if encoding_table and data_name in encoding_table: + sign_bit = encoding_table[data_name] + ids_values[id_] = encode_sign_magnitude(ids_values[id_], sign_bit) + + return ids_values + + def _decode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]: + for id_ in ids_values: + model = self._id_to_model(id_) + encoding_table = self.model_encoding_table.get(model) + if encoding_table and data_name in encoding_table: + sign_bit = encoding_table[data_name] + ids_values[id_] = decode_sign_magnitude(ids_values[id_], sign_bit) + + return ids_values + + def _split_into_byte_chunks(self, value: int, length: int) -> list[int]: + return _split_into_byte_chunks(value, length) + + def _broadcast_ping(self) -> tuple[dict[int, int], int]: + import scservo_sdk as scs + + data_list = {} + + status_length = 6 + + rx_length = 0 + wait_length = status_length * scs.MAX_ID + + txpacket = [0] * 6 + + tx_time_per_byte = (1000.0 / self.port_handler.getBaudRate()) * 10.0 + + txpacket[scs.PKT_ID] = scs.BROADCAST_ID + txpacket[scs.PKT_LENGTH] = 2 + txpacket[scs.PKT_INSTRUCTION] = scs.INST_PING + + result = self.packet_handler.txPacket(self.port_handler, txpacket) + if result != scs.COMM_SUCCESS: + self.port_handler.is_using = False + return data_list, result + + # set rx timeout + self.port_handler.setPacketTimeoutMillis((wait_length * tx_time_per_byte) + (3.0 * scs.MAX_ID) + 16.0) + + rxpacket = [] + while not self.port_handler.isPacketTimeout() and rx_length < wait_length: + rxpacket += self.port_handler.readPort(wait_length - rx_length) + rx_length = len(rxpacket) + + self.port_handler.is_using = False + + if rx_length == 0: + return data_list, scs.COMM_RX_TIMEOUT + + while True: + if rx_length < status_length: + return data_list, scs.COMM_RX_CORRUPT + + # find packet header + for idx in range(0, (rx_length - 1)): + if (rxpacket[idx] == 0xFF) and (rxpacket[idx + 1] == 0xFF): + break + + if idx == 0: # found at the beginning of the packet + # calculate checksum + checksum = 0 + for idx in range(2, status_length - 1): # except header & checksum + checksum += rxpacket[idx] + + checksum = ~checksum & 0xFF + if rxpacket[status_length - 1] == checksum: + result = scs.COMM_SUCCESS + data_list[rxpacket[scs.PKT_ID]] = rxpacket[scs.PKT_ERROR] + + del rxpacket[0:status_length] + rx_length = rx_length - status_length + + if rx_length == 0: + return data_list, result + else: + result = scs.COMM_RX_CORRUPT + # remove header (0xFF 0xFF) + del rxpacket[0:2] + rx_length = rx_length - 2 + else: + # remove unnecessary packets + del rxpacket[0:idx] + rx_length = rx_length - idx + + def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None: + self._assert_protocol_is_compatible("broadcast_ping") + for n_try in range(1 + num_retry): + ids_status, comm = self._broadcast_ping() + if self._is_comm_success(comm): + break + logger.debug(f"Broadcast ping failed on port '{self.port}' ({n_try=})") + logger.debug(self.packet_handler.getTxRxResult(comm)) + + if not self._is_comm_success(comm): + if raise_on_error: + raise ConnectionError(self.packet_handler.getTxRxResult(comm)) + return + + ids_errors = {id_: status for id_, status in ids_status.items() if self._is_error(status)} + if ids_errors: + display_dict = {id_: self.packet_handler.getRxPacketError(err) for id_, err in ids_errors.items()} + logger.error(f"Some motors found returned an error status:\n{pformat(display_dict, indent=4)}") + + return self._read_model_number(list(ids_status), raise_on_error) + + def _read_firmware_version(self, motor_ids: list[int], raise_on_error: bool = False) -> dict[int, str]: + firmware_versions = {} + for id_ in motor_ids: + firm_ver_major, comm, error = self._read( + *FIRMWARE_MAJOR_VERSION, id_, raise_on_error=raise_on_error + ) + if not self._is_comm_success(comm) or self._is_error(error): + continue + + firm_ver_minor, comm, error = self._read( + *FIRMWARE_MINOR_VERSION, id_, raise_on_error=raise_on_error + ) + if not self._is_comm_success(comm) or self._is_error(error): + continue + + firmware_versions[id_] = f"{firm_ver_major}.{firm_ver_minor}" + + return firmware_versions + + def _read_model_number(self, motor_ids: list[int], raise_on_error: bool = False) -> dict[int, int]: + model_numbers = {} + for id_ in motor_ids: + model_nb, comm, error = self._read(*MODEL_NUMBER, id_, raise_on_error=raise_on_error) + if not self._is_comm_success(comm) or self._is_error(error): + continue + + model_numbers[id_] = model_nb + + return model_numbers diff --git a/lerobot/common/motors/feetech/tables.py b/lerobot/common/motors/feetech/tables.py new file mode 100644 index 0000000000000000000000000000000000000000..d743c5dd90982b9078159c169aadddfc92ba1c12 --- /dev/null +++ b/lerobot/common/motors/feetech/tables.py @@ -0,0 +1,252 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +FIRMWARE_MAJOR_VERSION = (0, 1) +FIRMWARE_MINOR_VERSION = (1, 1) +MODEL_NUMBER = (3, 2) + +# TODO(Steven): Consider doing the following: +# from enum import Enum +# class MyControlTableKey(Enum): +# ID = "ID" +# GOAL_SPEED = "Goal_Speed" +# ... +# +# MY_CONTROL_TABLE ={ +# MyControlTableKey.ID.value: (5,1) +# MyControlTableKey.GOAL_SPEED.value: (46, 2) +# ... +# } +# This allows me do to: +# bus.write(MyControlTableKey.GOAL_SPEED, ...) +# Instead of: +# bus.write("Goal_Speed", ...) +# This is important for two reasons: +# 1. The linter will tell me if I'm trying to use an invalid key, instead of me realizing when I get the RunTimeError +# 2. We can change the value of the MyControlTableKey enums without impacting the client code + +# data_name: (address, size_byte) +# http://doc.feetech.cn/#/prodinfodownload?srcType=FT-SMS-STS-emanual-229f4476422d4059abfb1cb0 +STS_SMS_SERIES_CONTROL_TABLE = { + # EPROM + "Firmware_Major_Version": FIRMWARE_MAJOR_VERSION, # read-only + "Firmware_Minor_Version": FIRMWARE_MINOR_VERSION, # read-only + "Model_Number": MODEL_NUMBER, # read-only + "ID": (5, 1), + "Baud_Rate": (6, 1), + "Return_Delay_Time": (7, 1), + "Response_Status_Level": (8, 1), + "Min_Position_Limit": (9, 2), + "Max_Position_Limit": (11, 2), + "Max_Temperature_Limit": (13, 1), + "Max_Voltage_Limit": (14, 1), + "Min_Voltage_Limit": (15, 1), + "Max_Torque_Limit": (16, 2), + "Phase": (18, 1), + "Unloading_Condition": (19, 1), + "LED_Alarm_Condition": (20, 1), + "P_Coefficient": (21, 1), + "D_Coefficient": (22, 1), + "I_Coefficient": (23, 1), + "Minimum_Startup_Force": (24, 2), + "CW_Dead_Zone": (26, 1), + "CCW_Dead_Zone": (27, 1), + "Protection_Current": (28, 2), + "Angular_Resolution": (30, 1), + "Homing_Offset": (31, 2), + "Operating_Mode": (33, 1), + "Protective_Torque": (34, 1), + "Protection_Time": (35, 1), + "Overload_Torque": (36, 1), + "Velocity_closed_loop_P_proportional_coefficient": (37, 1), + "Over_Current_Protection_Time": (38, 1), + "Velocity_closed_loop_I_integral_coefficient": (39, 1), + # SRAM + "Torque_Enable": (40, 1), + "Acceleration": (41, 1), + "Goal_Position": (42, 2), + "Goal_Time": (44, 2), + "Goal_Velocity": (46, 2), + "Torque_Limit": (48, 2), + "Lock": (55, 1), + "Present_Position": (56, 2), # read-only + "Present_Velocity": (58, 2), # read-only + "Present_Load": (60, 2), # read-only + "Present_Voltage": (62, 1), # read-only + "Present_Temperature": (63, 1), # read-only + "Status": (65, 1), # read-only + "Moving": (66, 1), # read-only + "Present_Current": (69, 2), # read-only + "Goal_Position_2": (71, 2), # read-only + # Factory + "Moving_Velocity": (80, 1), + "Moving_Velocity_Threshold": (80, 1), + "DTs": (81, 1), # (ms) + "Velocity_Unit_factor": (82, 1), + "Hts": (83, 1), # (ns) valid for firmware >= 2.54, other versions keep 0 + "Maximum_Velocity_Limit": (84, 1), + "Maximum_Acceleration": (85, 1), + "Acceleration_Multiplier ": (86, 1), # Acceleration multiplier in effect when acceleration is 0 +} + +# http://doc.feetech.cn/#/prodinfodownload?srcType=FT-SCSCL-emanual-cbcc8ab2e3384282a01d4bf3 +SCS_SERIES_CONTROL_TABLE = { + # EPROM + "Firmware_Major_Version": FIRMWARE_MAJOR_VERSION, # read-only + "Firmware_Minor_Version": FIRMWARE_MINOR_VERSION, # read-only + "Model_Number": MODEL_NUMBER, # read-only + "ID": (5, 1), + "Baud_Rate": (6, 1), + "Return_Delay_Time": (7, 1), + "Response_Status_Level": (8, 1), + "Min_Position_Limit": (9, 2), + "Max_Position_Limit": (11, 2), + "Max_Temperature_Limit": (13, 1), + "Max_Voltage_Limit": (14, 1), + "Min_Voltage_Limit": (15, 1), + "Max_Torque_Limit": (16, 2), + "Phase": (18, 1), + "Unloading_Condition": (19, 1), + "LED_Alarm_Condition": (20, 1), + "P_Coefficient": (21, 1), + "D_Coefficient": (22, 1), + "I_Coefficient": (23, 1), + "Minimum_Startup_Force": (24, 2), + "CW_Dead_Zone": (26, 1), + "CCW_Dead_Zone": (27, 1), + "Protective_Torque": (37, 1), + "Protection_Time": (38, 1), + # SRAM + "Torque_Enable": (40, 1), + "Acceleration": (41, 1), + "Goal_Position": (42, 2), + "Running_Time": (44, 2), + "Goal_Velocity": (46, 2), + "Lock": (48, 1), + "Present_Position": (56, 2), # read-only + "Present_Velocity": (58, 2), # read-only + "Present_Load": (60, 2), # read-only + "Present_Voltage": (62, 1), # read-only + "Present_Temperature": (63, 1), # read-only + "Sync_Write_Flag": (64, 1), # read-only + "Status": (65, 1), # read-only + "Moving": (66, 1), # read-only + # Factory + "PWM_Maximum_Step": (78, 1), + "Moving_Velocity_Threshold*50": (79, 1), + "DTs": (80, 1), # (ms) + "Minimum_Velocity_Limit*50": (81, 1), + "Maximum_Velocity_Limit*50": (82, 1), + "Acceleration_2": (83, 1), # don't know what that is +} + +STS_SMS_SERIES_BAUDRATE_TABLE = { + 1_000_000: 0, + 500_000: 1, + 250_000: 2, + 128_000: 3, + 115_200: 4, + 57_600: 5, + 38_400: 6, + 19_200: 7, +} + +SCS_SERIES_BAUDRATE_TABLE = { + 1_000_000: 0, + 500_000: 1, + 250_000: 2, + 128_000: 3, + 115_200: 4, + 57_600: 5, + 38_400: 6, + 19_200: 7, +} + +MODEL_CONTROL_TABLE = { + "sts_series": STS_SMS_SERIES_CONTROL_TABLE, + "scs_series": SCS_SERIES_CONTROL_TABLE, + "sms_series": STS_SMS_SERIES_CONTROL_TABLE, + "sts3215": STS_SMS_SERIES_CONTROL_TABLE, + "sts3250": STS_SMS_SERIES_CONTROL_TABLE, + "scs0009": SCS_SERIES_CONTROL_TABLE, + "sm8512bl": STS_SMS_SERIES_CONTROL_TABLE, +} + +MODEL_RESOLUTION = { + "sts_series": 4096, + "sms_series": 4096, + "scs_series": 1024, + "sts3215": 4096, + "sts3250": 4096, + "sm8512bl": 65536, + "scs0009": 1024, +} + +MODEL_BAUDRATE_TABLE = { + "sts_series": STS_SMS_SERIES_BAUDRATE_TABLE, + "sms_series": STS_SMS_SERIES_BAUDRATE_TABLE, + "scs_series": SCS_SERIES_BAUDRATE_TABLE, + "sm8512bl": STS_SMS_SERIES_BAUDRATE_TABLE, + "sts3215": STS_SMS_SERIES_BAUDRATE_TABLE, + "sts3250": STS_SMS_SERIES_BAUDRATE_TABLE, + "scs0009": SCS_SERIES_BAUDRATE_TABLE, +} + +# Sign-Magnitude encoding bits +STS_SMS_SERIES_ENCODINGS_TABLE = { + "Homing_Offset": 11, + "Goal_Velocity": 15, + "Present_Velocity": 15, +} + +MODEL_ENCODING_TABLE = { + "sts_series": STS_SMS_SERIES_ENCODINGS_TABLE, + "sms_series": STS_SMS_SERIES_ENCODINGS_TABLE, + "scs_series": {}, + "sts3215": STS_SMS_SERIES_ENCODINGS_TABLE, + "sts3250": STS_SMS_SERIES_ENCODINGS_TABLE, + "sm8512bl": STS_SMS_SERIES_ENCODINGS_TABLE, + "scs0009": {}, +} + +SCAN_BAUDRATES = [ + 4_800, + 9_600, + 14_400, + 19_200, + 38_400, + 57_600, + 115_200, + 128_000, + 250_000, + 500_000, + 1_000_000, +] + +MODEL_NUMBER_TABLE = { + "sts3215": 777, + "sts3250": 2825, + "sm8512bl": 11272, + "scs0009": 1284, +} + +MODEL_PROTOCOL = { + "sts_series": 0, + "sms_series": 0, + "scs_series": 1, + "sts3215": 0, + "sts3250": 0, + "sm8512bl": 0, + "scs0009": 1, +} diff --git a/lerobot/common/motors/motors_bus.py b/lerobot/common/motors/motors_bus.py new file mode 100644 index 0000000000000000000000000000000000000000..9b872e3fb0e07a00e733604a0ec4bc905aeb5ade --- /dev/null +++ b/lerobot/common/motors/motors_bus.py @@ -0,0 +1,1219 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# ruff: noqa: N802 +# This noqa is for the Protocols classes: PortHandler, PacketHandler GroupSyncRead/Write +# TODO(aliberts): Add block noqa when feature below is available +# https://github.com/astral-sh/ruff/issues/3711 + +import abc +import logging +from contextlib import contextmanager +from dataclasses import dataclass +from enum import Enum +from functools import cached_property +from pprint import pformat +from typing import Protocol, TypeAlias + +import serial +from deepdiff import DeepDiff +from tqdm import tqdm + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.utils.utils import enter_pressed, move_cursor_up + +NameOrID: TypeAlias = str | int +Value: TypeAlias = int | float + +logger = logging.getLogger(__name__) + + +def get_ctrl_table(model_ctrl_table: dict[str, dict], model: str) -> dict[str, tuple[int, int]]: + ctrl_table = model_ctrl_table.get(model) + if ctrl_table is None: + raise KeyError(f"Control table for {model=} not found.") + return ctrl_table + + +def get_address(model_ctrl_table: dict[str, dict], model: str, data_name: str) -> tuple[int, int]: + ctrl_table = get_ctrl_table(model_ctrl_table, model) + addr_bytes = ctrl_table.get(data_name) + if addr_bytes is None: + raise KeyError(f"Address for '{data_name}' not found in {model} control table.") + return addr_bytes + + +def assert_same_address(model_ctrl_table: dict[str, dict], motor_models: list[str], data_name: str) -> None: + all_addr = [] + all_bytes = [] + for model in motor_models: + addr, bytes = get_address(model_ctrl_table, model, data_name) + all_addr.append(addr) + all_bytes.append(bytes) + + if len(set(all_addr)) != 1: + raise NotImplementedError( + f"At least two motor models use a different address for `data_name`='{data_name}'" + f"({list(zip(motor_models, all_addr, strict=False))})." + ) + + if len(set(all_bytes)) != 1: + raise NotImplementedError( + f"At least two motor models use a different bytes representation for `data_name`='{data_name}'" + f"({list(zip(motor_models, all_bytes, strict=False))})." + ) + + +class MotorNormMode(str, Enum): + RANGE_0_100 = "range_0_100" + RANGE_M100_100 = "range_m100_100" + DEGREES = "degrees" + + +@dataclass +class MotorCalibration: + id: int + drive_mode: int + homing_offset: int + range_min: int + range_max: int + + +@dataclass +class Motor: + id: int + model: str + norm_mode: MotorNormMode + + +class JointOutOfRangeError(Exception): + def __init__(self, message="Joint is out of range"): + self.message = message + super().__init__(self.message) + + +class PortHandler(Protocol): + def __init__(self, port_name): + self.is_open: bool + self.baudrate: int + self.packet_start_time: float + self.packet_timeout: float + self.tx_time_per_byte: float + self.is_using: bool + self.port_name: str + self.ser: serial.Serial + + def openPort(self): ... + def closePort(self): ... + def clearPort(self): ... + def setPortName(self, port_name): ... + def getPortName(self): ... + def setBaudRate(self, baudrate): ... + def getBaudRate(self): ... + def getBytesAvailable(self): ... + def readPort(self, length): ... + def writePort(self, packet): ... + def setPacketTimeout(self, packet_length): ... + def setPacketTimeoutMillis(self, msec): ... + def isPacketTimeout(self): ... + def getCurrentTime(self): ... + def getTimeSinceStart(self): ... + def setupPort(self, cflag_baud): ... + def getCFlagBaud(self, baudrate): ... + + +class PacketHandler(Protocol): + def getTxRxResult(self, result): ... + def getRxPacketError(self, error): ... + def txPacket(self, port, txpacket): ... + def rxPacket(self, port): ... + def txRxPacket(self, port, txpacket): ... + def ping(self, port, id): ... + def action(self, port, id): ... + def readTx(self, port, id, address, length): ... + def readRx(self, port, id, length): ... + def readTxRx(self, port, id, address, length): ... + def read1ByteTx(self, port, id, address): ... + def read1ByteRx(self, port, id): ... + def read1ByteTxRx(self, port, id, address): ... + def read2ByteTx(self, port, id, address): ... + def read2ByteRx(self, port, id): ... + def read2ByteTxRx(self, port, id, address): ... + def read4ByteTx(self, port, id, address): ... + def read4ByteRx(self, port, id): ... + def read4ByteTxRx(self, port, id, address): ... + def writeTxOnly(self, port, id, address, length, data): ... + def writeTxRx(self, port, id, address, length, data): ... + def write1ByteTxOnly(self, port, id, address, data): ... + def write1ByteTxRx(self, port, id, address, data): ... + def write2ByteTxOnly(self, port, id, address, data): ... + def write2ByteTxRx(self, port, id, address, data): ... + def write4ByteTxOnly(self, port, id, address, data): ... + def write4ByteTxRx(self, port, id, address, data): ... + def regWriteTxOnly(self, port, id, address, length, data): ... + def regWriteTxRx(self, port, id, address, length, data): ... + def syncReadTx(self, port, start_address, data_length, param, param_length): ... + def syncWriteTxOnly(self, port, start_address, data_length, param, param_length): ... + + +class GroupSyncRead(Protocol): + def __init__(self, port, ph, start_address, data_length): + self.port: str + self.ph: PortHandler + self.start_address: int + self.data_length: int + self.last_result: bool + self.is_param_changed: bool + self.param: list + self.data_dict: dict + + def makeParam(self): ... + def addParam(self, id): ... + def removeParam(self, id): ... + def clearParam(self): ... + def txPacket(self): ... + def rxPacket(self): ... + def txRxPacket(self): ... + def isAvailable(self, id, address, data_length): ... + def getData(self, id, address, data_length): ... + + +class GroupSyncWrite(Protocol): + def __init__(self, port, ph, start_address, data_length): + self.port: str + self.ph: PortHandler + self.start_address: int + self.data_length: int + self.is_param_changed: bool + self.param: list + self.data_dict: dict + + def makeParam(self): ... + def addParam(self, id, data): ... + def removeParam(self, id): ... + def changeParam(self, id, data): ... + def clearParam(self): ... + def txPacket(self): ... + + +class MotorsBus(abc.ABC): + """ + A MotorsBus allows to efficiently read and write to the attached motors. + It represents several motors daisy-chained together and connected through a serial port. + There are currently two implementations of this abstract class: + - DynamixelMotorsBus + - FeetechMotorsBus + + Note: This class may evolve in the future should we add support for other types of bus. + + A MotorsBus subclass instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)). + To find the port, you can run our utility script: + ```bash + python -m lerobot.find_port.py + >>> Finding all available ports for the MotorsBus. + >>> ['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751'] + >>> Remove the usb cable from your MotorsBus and press Enter when done. + >>> The port of this MotorsBus is /dev/tty.usbmodem575E0031751. + >>> Reconnect the usb cable. + ``` + + Example of usage for 1 Feetech sts3215 motor connected to the bus: + ```python + bus = FeetechMotorsBus( + port="/dev/tty.usbmodem575E0031751", + motors={"my_motor": (1, "sts3215")}, + ) + bus.connect() + + position = bus.read("Present_Position", "my_motor", normalize=False) + + # Move from a few motor steps as an example + few_steps = 30 + bus.write("Goal_Position", "my_motor", position + few_steps, normalize=False) + + # When done, properly disconnect the port using + bus.disconnect() + ``` + """ + + apply_drive_mode: bool + available_baudrates: list[int] + default_baudrate: int + default_timeout: int + model_baudrate_table: dict[str, dict] + model_ctrl_table: dict[str, dict] + model_encoding_table: dict[str, dict] + model_number_table: dict[str, int] + model_resolution_table: dict[str, int] + normalized_data: list[str] + + def __init__( + self, + port: str, + motors: dict[str, Motor], + calibration: dict[str, MotorCalibration] | None = None, + ): + self.port = port + self.motors = motors + self.calibration = calibration if calibration else {} + + self.port_handler: PortHandler + self.packet_handler: PacketHandler + self.sync_reader: GroupSyncRead + self.sync_writer: GroupSyncWrite + self._comm_success: int + self._no_error: int + + self._id_to_model_dict = {m.id: m.model for m in self.motors.values()} + self._id_to_name_dict = {m.id: motor for motor, m in self.motors.items()} + self._model_nb_to_model_dict = {v: k for k, v in self.model_number_table.items()} + + self._validate_motors() + + def __len__(self): + return len(self.motors) + + def __repr__(self): + return ( + f"{self.__class__.__name__}(\n" + f" Port: '{self.port}',\n" + f" Motors: \n{pformat(self.motors, indent=8, sort_dicts=False)},\n" + ")',\n" + ) + + @cached_property + def _has_different_ctrl_tables(self) -> bool: + if len(self.models) < 2: + return False + + first_table = self.model_ctrl_table[self.models[0]] + return any( + DeepDiff(first_table, get_ctrl_table(self.model_ctrl_table, model)) for model in self.models[1:] + ) + + @cached_property + def models(self) -> list[str]: + return [m.model for m in self.motors.values()] + + @cached_property + def ids(self) -> list[int]: + return [m.id for m in self.motors.values()] + + def _model_nb_to_model(self, motor_nb: int) -> str: + return self._model_nb_to_model_dict[motor_nb] + + def _id_to_model(self, motor_id: int) -> str: + return self._id_to_model_dict[motor_id] + + def _id_to_name(self, motor_id: int) -> str: + return self._id_to_name_dict[motor_id] + + def _get_motor_id(self, motor: NameOrID) -> int: + if isinstance(motor, str): + return self.motors[motor].id + elif isinstance(motor, int): + return motor + else: + raise TypeError(f"'{motor}' should be int, str.") + + def _get_motor_model(self, motor: NameOrID) -> int: + if isinstance(motor, str): + return self.motors[motor].model + elif isinstance(motor, int): + return self._id_to_model_dict[motor] + else: + raise TypeError(f"'{motor}' should be int, str.") + + def _get_motors_list(self, motors: str | list[str] | None) -> list[str]: + if motors is None: + return list(self.motors) + elif isinstance(motors, str): + return [motors] + elif isinstance(motors, list): + return motors.copy() + else: + raise TypeError(motors) + + def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]: + if isinstance(values, (int, float)): + return dict.fromkeys(self.ids, values) + elif isinstance(values, dict): + return {self.motors[motor].id: val for motor, val in values.items()} + else: + raise TypeError(f"'values' is expected to be a single value or a dict. Got {values}") + + def _validate_motors(self) -> None: + if len(self.ids) != len(set(self.ids)): + raise ValueError(f"Some motors have the same id!\n{self}") + + # Ensure ctrl table available for all models + for model in self.models: + get_ctrl_table(self.model_ctrl_table, model) + + def _is_comm_success(self, comm: int) -> bool: + return comm == self._comm_success + + def _is_error(self, error: int) -> bool: + return error != self._no_error + + def _assert_motors_exist(self) -> None: + expected_models = {m.id: self.model_number_table[m.model] for m in self.motors.values()} + + found_models = {} + for id_ in self.ids: + model_nb = self.ping(id_) + if model_nb is not None: + found_models[id_] = model_nb + + missing_ids = [id_ for id_ in self.ids if id_ not in found_models] + wrong_models = { + id_: (expected_models[id_], found_models[id_]) + for id_ in found_models + if expected_models.get(id_) != found_models[id_] + } + + if missing_ids or wrong_models: + error_lines = [f"{self.__class__.__name__} motor check failed on port '{self.port}':"] + + if missing_ids: + error_lines.append("\nMissing motor IDs:") + error_lines.extend( + f" - {id_} (expected model: {expected_models[id_]})" for id_ in missing_ids + ) + + if wrong_models: + error_lines.append("\nMotors with incorrect model numbers:") + error_lines.extend( + f" - {id_} ({self._id_to_name(id_)}): expected {expected}, found {found}" + for id_, (expected, found) in wrong_models.items() + ) + + error_lines.append("\nFull expected motor list (id: model_number):") + error_lines.append(pformat(expected_models, indent=4, sort_dicts=False)) + error_lines.append("\nFull found motor list (id: model_number):") + error_lines.append(pformat(found_models, indent=4, sort_dicts=False)) + + raise RuntimeError("\n".join(error_lines)) + + @abc.abstractmethod + def _assert_protocol_is_compatible(self, instruction_name: str) -> None: + pass + + @property + def is_connected(self) -> bool: + """bool: `True` if the underlying serial port is open.""" + return self.port_handler.is_open + + def connect(self, handshake: bool = True) -> None: + """Open the serial port and initialise communication. + + Args: + handshake (bool, optional): Pings every expected motor and performs additional + integrity checks specific to the implementation. Defaults to `True`. + + Raises: + DeviceAlreadyConnectedError: The port is already open. + ConnectionError: The underlying SDK failed to open the port or the handshake did not succeed. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError( + f"{self.__class__.__name__}('{self.port}') is already connected. Do not call `{self.__class__.__name__}.connect()` twice." + ) + + self._connect(handshake) + self.set_timeout() + logger.debug(f"{self.__class__.__name__} connected.") + + def _connect(self, handshake: bool = True) -> None: + try: + if not self.port_handler.openPort(): + raise OSError(f"Failed to open port '{self.port}'.") + elif handshake: + self._handshake() + except (FileNotFoundError, OSError, serial.SerialException) as e: + raise ConnectionError( + f"\nCould not connect on port '{self.port}'. Make sure you are using the correct port." + "\nTry running `python lerobot/find_port.py`\n" + ) from e + + @abc.abstractmethod + def _handshake(self) -> None: + pass + + def disconnect(self, disable_torque: bool = True) -> None: + """Close the serial port (optionally disabling torque first). + + Args: + disable_torque (bool, optional): If `True` (default) torque is disabled on every motor before + closing the port. This can prevent damaging motors if they are left applying resisting torque + after disconnect. + """ + if not self.is_connected: + raise DeviceNotConnectedError( + f"{self.__class__.__name__}('{self.port}') is not connected. Try running `{self.__class__.__name__}.connect()` first." + ) + + if disable_torque: + self.port_handler.clearPort() + self.port_handler.is_using = False + self.disable_torque(num_retry=5) + + self.port_handler.closePort() + logger.debug(f"{self.__class__.__name__} disconnected.") + + @classmethod + def scan_port(cls, port: str, *args, **kwargs) -> dict[int, list[int]]: + """Probe *port* at every supported baud-rate and list responding IDs. + + Args: + port (str): Serial/USB port to scan (e.g. ``"/dev/ttyUSB0"``). + *args, **kwargs: Forwarded to the subclass constructor. + + Returns: + dict[int, list[int]]: Mapping *baud-rate → list of motor IDs* + for every baud-rate that produced at least one response. + """ + bus = cls(port, {}, *args, **kwargs) + bus._connect(handshake=False) + baudrate_ids = {} + for baudrate in tqdm(bus.available_baudrates, desc="Scanning port"): + bus.set_baudrate(baudrate) + ids_models = bus.broadcast_ping() + if ids_models: + tqdm.write(f"Motors found for {baudrate=}: {pformat(ids_models, indent=4)}") + baudrate_ids[baudrate] = list(ids_models) + + bus.port_handler.closePort() + return baudrate_ids + + def setup_motor( + self, motor: str, initial_baudrate: int | None = None, initial_id: int | None = None + ) -> None: + """Assign the correct ID and baud-rate to a single motor. + + This helper temporarily switches to the motor's current settings, disables torque, sets the desired + ID, and finally programs the bus' default baud-rate. + + Args: + motor (str): Key of the motor in :pyattr:`motors`. + initial_baudrate (int | None, optional): Current baud-rate (skips scanning when provided). + Defaults to None. + initial_id (int | None, optional): Current ID (skips scanning when provided). Defaults to None. + + Raises: + RuntimeError: The motor could not be found or its model number + does not match the expected one. + ConnectionError: Communication with the motor failed. + """ + if not self.is_connected: + self._connect(handshake=False) + + if initial_baudrate is None: + initial_baudrate, initial_id = self._find_single_motor(motor) + + if initial_id is None: + _, initial_id = self._find_single_motor(motor, initial_baudrate) + + model = self.motors[motor].model + target_id = self.motors[motor].id + self.set_baudrate(initial_baudrate) + self._disable_torque(initial_id, model) + + # Set ID + addr, length = get_address(self.model_ctrl_table, model, "ID") + self._write(addr, length, initial_id, target_id) + + # Set Baudrate + addr, length = get_address(self.model_ctrl_table, model, "Baud_Rate") + baudrate_value = self.model_baudrate_table[model][self.default_baudrate] + self._write(addr, length, target_id, baudrate_value) + + self.set_baudrate(self.default_baudrate) + + @abc.abstractmethod + def _find_single_motor(self, motor: str, initial_baudrate: int | None) -> tuple[int, int]: + pass + + @abc.abstractmethod + def configure_motors(self) -> None: + """Write implementation-specific recommended settings to every motor. + + Typical changes include shortening the return delay, increasing + acceleration limits or disabling safety locks. + """ + pass + + @abc.abstractmethod + def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None: + """Disable torque on selected motors. + + Disabling Torque allows to write to the motors' permanent memory area (EPROM/EEPROM). + + Args: + motors (int | str | list[str] | None, optional): Target motors. Accepts a motor name, an ID, a + list of names or `None` to affect every registered motor. Defaults to `None`. + num_retry (int, optional): Number of additional retry attempts on communication failure. + Defaults to 0. + """ + pass + + @abc.abstractmethod + def _disable_torque(self, motor: int, model: str, num_retry: int = 0) -> None: + pass + + @abc.abstractmethod + def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None: + """Enable torque on selected motors. + + Args: + motor (int): Same semantics as :pymeth:`disable_torque`. Defaults to `None`. + num_retry (int, optional): Number of additional retry attempts on communication failure. + Defaults to 0. + """ + pass + + @contextmanager + def torque_disabled(self): + """Context-manager that guarantees torque is re-enabled. + + This helper is useful to temporarily disable torque when configuring motors. + + Examples: + >>> with bus.torque_disabled(): + ... # Safe operations here + ... pass + """ + self.disable_torque() + try: + yield + finally: + self.enable_torque() + + def set_timeout(self, timeout_ms: int | None = None): + """Change the packet timeout used by the SDK. + + Args: + timeout_ms (int | None, optional): Timeout in *milliseconds*. If `None` (default) the method falls + back to :pyattr:`default_timeout`. + """ + timeout_ms = timeout_ms if timeout_ms is not None else self.default_timeout + self.port_handler.setPacketTimeoutMillis(timeout_ms) + + def get_baudrate(self) -> int: + """Return the current baud-rate configured on the port. + + Returns: + int: Baud-rate in bits / second. + """ + return self.port_handler.getBaudRate() + + def set_baudrate(self, baudrate: int) -> None: + """Set a new UART baud-rate on the port. + + Args: + baudrate (int): Desired baud-rate in bits / second. + + Raises: + RuntimeError: The SDK failed to apply the change. + """ + present_bus_baudrate = self.port_handler.getBaudRate() + if present_bus_baudrate != baudrate: + logger.info(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.") + self.port_handler.setBaudRate(baudrate) + + if self.port_handler.getBaudRate() != baudrate: + raise RuntimeError("Failed to write bus baud rate.") + + @property + @abc.abstractmethod + def is_calibrated(self) -> bool: + """bool: ``True`` if the cached calibration matches the motors.""" + pass + + @abc.abstractmethod + def read_calibration(self) -> dict[str, MotorCalibration]: + """Read calibration parameters from the motors. + + Returns: + dict[str, MotorCalibration]: Mapping *motor name → calibration*. + """ + pass + + @abc.abstractmethod + def write_calibration(self, calibration_dict: dict[str, MotorCalibration]) -> None: + """Write calibration parameters to the motors and cache them. + + Args: + calibration_dict (dict[str, MotorCalibration]): Calibration obtained from + :pymeth:`read_calibration` or crafted by the user. + """ + pass + + def reset_calibration(self, motors: NameOrID | list[NameOrID] | None = None) -> None: + """Restore factory calibration for the selected motors. + + Homing offset is set to ``0`` and min/max position limits are set to the full usable range. + The in-memory :pyattr:`calibration` is cleared. + + Args: + motors (NameOrID | list[NameOrID] | None, optional): Selection of motors. `None` (default) + resets every motor. + """ + if motors is None: + motors = list(self.motors) + elif isinstance(motors, (str, int)): + motors = [motors] + elif not isinstance(motors, list): + raise TypeError(motors) + + for motor in motors: + model = self._get_motor_model(motor) + max_res = self.model_resolution_table[model] - 1 + self.write("Homing_Offset", motor, 0, normalize=False) + self.write("Min_Position_Limit", motor, 0, normalize=False) + self.write("Max_Position_Limit", motor, max_res, normalize=False) + + self.calibration = {} + + def set_half_turn_homings(self, motors: NameOrID | list[NameOrID] | None = None) -> dict[NameOrID, Value]: + """Centre each motor range around its current position. + + The function computes and writes a homing offset such that the present position becomes exactly one + half-turn (e.g. `2047` on a 12-bit encoder). + + Args: + motors (NameOrID | list[NameOrID] | None, optional): Motors to adjust. Defaults to all motors (`None`). + + Returns: + dict[NameOrID, Value]: Mapping *motor → written homing offset*. + """ + if motors is None: + motors = list(self.motors) + elif isinstance(motors, (str, int)): + motors = [motors] + elif not isinstance(motors, list): + raise TypeError(motors) + + self.reset_calibration(motors) + actual_positions = self.sync_read("Present_Position", motors, normalize=False) + homing_offsets = self._get_half_turn_homings(actual_positions) + for motor, offset in homing_offsets.items(): + self.write("Homing_Offset", motor, offset) + + return homing_offsets + + @abc.abstractmethod + def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]: + pass + + def record_ranges_of_motion( + self, motors: NameOrID | list[NameOrID] | None = None, display_values: bool = True + ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]: + """Interactively record the min/max encoder values of each motor. + + Move the joints by hand (with torque disabled) while the method streams live positions. Press + :kbd:`Enter` to finish. + + Args: + motors (NameOrID | list[NameOrID] | None, optional): Motors to record. + Defaults to every motor (`None`). + display_values (bool, optional): When `True` (default) a live table is printed to the console. + + Returns: + tuple[dict[NameOrID, Value], dict[NameOrID, Value]]: Two dictionaries *mins* and *maxes* with the + extreme values observed for each motor. + """ + if motors is None: + motors = list(self.motors) + elif isinstance(motors, (str, int)): + motors = [motors] + elif not isinstance(motors, list): + raise TypeError(motors) + + start_positions = self.sync_read("Present_Position", motors, normalize=False) + mins = start_positions.copy() + maxes = start_positions.copy() + + user_pressed_enter = False + while not user_pressed_enter: + positions = self.sync_read("Present_Position", motors, normalize=False) + mins = {motor: min(positions[motor], min_) for motor, min_ in mins.items()} + maxes = {motor: max(positions[motor], max_) for motor, max_ in maxes.items()} + + if display_values: + print("\n-------------------------------------------") + print(f"{'NAME':<15} | {'MIN':>6} | {'POS':>6} | {'MAX':>6}") + for motor in motors: + print(f"{motor:<15} | {mins[motor]:>6} | {positions[motor]:>6} | {maxes[motor]:>6}") + + if enter_pressed(): + user_pressed_enter = True + + if display_values and not user_pressed_enter: + # Move cursor up to overwrite the previous output + move_cursor_up(len(motors) + 3) + + same_min_max = [motor for motor in motors if mins[motor] == maxes[motor]] + if same_min_max: + raise ValueError(f"Some motors have the same min and max values:\n{pformat(same_min_max)}") + + return mins, maxes + + def _normalize(self, ids_values: dict[int, int]) -> dict[int, float]: + if not self.calibration: + raise RuntimeError(f"{self} has no calibration registered.") + + normalized_values = {} + for id_, val in ids_values.items(): + motor = self._id_to_name(id_) + min_ = self.calibration[motor].range_min + max_ = self.calibration[motor].range_max + drive_mode = self.apply_drive_mode and self.calibration[motor].drive_mode + if max_ == min_: + raise ValueError(f"Invalid calibration for motor '{motor}': min and max are equal.") + + bounded_val = min(max_, max(min_, val)) + if self.motors[motor].norm_mode is MotorNormMode.RANGE_M100_100: + norm = (((bounded_val - min_) / (max_ - min_)) * 200) - 100 + normalized_values[id_] = -norm if drive_mode else norm + elif self.motors[motor].norm_mode is MotorNormMode.RANGE_0_100: + norm = ((bounded_val - min_) / (max_ - min_)) * 100 + normalized_values[id_] = 100 - norm if drive_mode else norm + elif self.motors[motor].norm_mode is MotorNormMode.DEGREES: + mid = (min_ + max_) / 2 + max_res = self.model_resolution_table[self._id_to_model(id_)] - 1 + normalized_values[id_] = (val - mid) * 360 / max_res + else: + raise NotImplementedError + + return normalized_values + + def _unnormalize(self, ids_values: dict[int, float]) -> dict[int, int]: + if not self.calibration: + raise RuntimeError(f"{self} has no calibration registered.") + + unnormalized_values = {} + for id_, val in ids_values.items(): + motor = self._id_to_name(id_) + min_ = self.calibration[motor].range_min + max_ = self.calibration[motor].range_max + drive_mode = self.apply_drive_mode and self.calibration[motor].drive_mode + if max_ == min_: + raise ValueError(f"Invalid calibration for motor '{motor}': min and max are equal.") + + if self.motors[motor].norm_mode is MotorNormMode.RANGE_M100_100: + val = -val if drive_mode else val + bounded_val = min(100.0, max(-100.0, val)) + unnormalized_values[id_] = int(((bounded_val + 100) / 200) * (max_ - min_) + min_) + elif self.motors[motor].norm_mode is MotorNormMode.RANGE_0_100: + val = 100 - val if drive_mode else val + bounded_val = min(100.0, max(0.0, val)) + unnormalized_values[id_] = int((bounded_val / 100) * (max_ - min_) + min_) + elif self.motors[motor].norm_mode is MotorNormMode.DEGREES: + mid = (min_ + max_) / 2 + max_res = self.model_resolution_table[self._id_to_model(id_)] - 1 + unnormalized_values[id_] = int((val * max_res / 360) + mid) + else: + raise NotImplementedError + + return unnormalized_values + + @abc.abstractmethod + def _encode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]: + pass + + @abc.abstractmethod + def _decode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]: + pass + + def _serialize_data(self, value: int, length: int) -> list[int]: + """ + Converts an unsigned integer value into a list of byte-sized integers to be sent via a communication + protocol. Depending on the protocol, split values can be in big-endian or little-endian order. + + Supported data length for both Feetech and Dynamixel: + - 1 (for values 0 to 255) + - 2 (for values 0 to 65,535) + - 4 (for values 0 to 4,294,967,295) + """ + if value < 0: + raise ValueError(f"Negative values are not allowed: {value}") + + max_value = {1: 0xFF, 2: 0xFFFF, 4: 0xFFFFFFFF}.get(length) + if max_value is None: + raise NotImplementedError(f"Unsupported byte size: {length}. Expected [1, 2, 4].") + + if value > max_value: + raise ValueError(f"Value {value} exceeds the maximum for {length} bytes ({max_value}).") + + return self._split_into_byte_chunks(value, length) + + @abc.abstractmethod + def _split_into_byte_chunks(self, value: int, length: int) -> list[int]: + """Convert an integer into a list of byte-sized integers.""" + pass + + def ping(self, motor: NameOrID, num_retry: int = 0, raise_on_error: bool = False) -> int | None: + """Ping a single motor and return its model number. + + Args: + motor (NameOrID): Target motor (name or ID). + num_retry (int, optional): Extra attempts before giving up. Defaults to `0`. + raise_on_error (bool, optional): If `True` communication errors raise exceptions instead of + returning `None`. Defaults to `False`. + + Returns: + int | None: Motor model number or `None` on failure. + """ + id_ = self._get_motor_id(motor) + for n_try in range(1 + num_retry): + model_number, comm, error = self.packet_handler.ping(self.port_handler, id_) + if self._is_comm_success(comm): + break + logger.debug(f"ping failed for {id_=}: {n_try=} got {comm=} {error=}") + + if not self._is_comm_success(comm): + if raise_on_error: + raise ConnectionError(self.packet_handler.getTxRxResult(comm)) + else: + return + if self._is_error(error): + if raise_on_error: + raise RuntimeError(self.packet_handler.getRxPacketError(error)) + else: + return + + return model_number + + @abc.abstractmethod + def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None: + """Ping every ID on the bus using the broadcast address. + + Args: + num_retry (int, optional): Retry attempts. Defaults to `0`. + raise_on_error (bool, optional): When `True` failures raise an exception instead of returning + `None`. Defaults to `False`. + + Returns: + dict[int, int] | None: Mapping *id → model number* or `None` if the call failed. + """ + pass + + def read( + self, + data_name: str, + motor: str, + *, + normalize: bool = True, + num_retry: int = 0, + ) -> Value: + """Read a register from a motor. + + Args: + data_name (str): Control-table key (e.g. `"Present_Position"`). + motor (str): Motor name. + normalize (bool, optional): When `True` (default) scale the value to a user-friendly range as + defined by the calibration. + num_retry (int, optional): Retry attempts. Defaults to `0`. + + Returns: + Value: Raw or normalised value depending on *normalize*. + """ + if not self.is_connected: + raise DeviceNotConnectedError( + f"{self.__class__.__name__}('{self.port}') is not connected. You need to run `{self.__class__.__name__}.connect()`." + ) + + id_ = self.motors[motor].id + model = self.motors[motor].model + addr, length = get_address(self.model_ctrl_table, model, data_name) + + err_msg = f"Failed to read '{data_name}' on {id_=} after {num_retry + 1} tries." + value, _, _ = self._read(addr, length, id_, num_retry=num_retry, raise_on_error=True, err_msg=err_msg) + + id_value = self._decode_sign(data_name, {id_: value}) + + if normalize and data_name in self.normalized_data: + id_value = self._normalize(id_value) + + return id_value[id_] + + def _read( + self, + address: int, + length: int, + motor_id: int, + *, + num_retry: int = 0, + raise_on_error: bool = True, + err_msg: str = "", + ) -> tuple[int, int]: + if length == 1: + read_fn = self.packet_handler.read1ByteTxRx + elif length == 2: + read_fn = self.packet_handler.read2ByteTxRx + elif length == 4: + read_fn = self.packet_handler.read4ByteTxRx + else: + raise ValueError(length) + + for n_try in range(1 + num_retry): + value, comm, error = read_fn(self.port_handler, motor_id, address) + if self._is_comm_success(comm): + break + logger.debug( + f"Failed to read @{address=} ({length=}) on {motor_id=} ({n_try=}): " + + self.packet_handler.getTxRxResult(comm) + ) + + if not self._is_comm_success(comm) and raise_on_error: + raise ConnectionError(f"{err_msg} {self.packet_handler.getTxRxResult(comm)}") + elif self._is_error(error) and raise_on_error: + raise RuntimeError(f"{err_msg} {self.packet_handler.getRxPacketError(error)}") + + return value, comm, error + + def write( + self, data_name: str, motor: str, value: Value, *, normalize: bool = True, num_retry: int = 0 + ) -> None: + """Write a value to a single motor's register. + + Contrary to :pymeth:`sync_write`, this expects a response status packet emitted by the motor, which + provides a guarantee that the value was written to the register successfully. In consequence, it is + slower than :pymeth:`sync_write` but it is more reliable. It should typically be used when configuring + motors. + + Args: + data_name (str): Register name. + motor (str): Motor name. + value (Value): Value to write. If *normalize* is `True` the value is first converted to raw + units. + normalize (bool, optional): Enable or disable normalisation. Defaults to `True`. + num_retry (int, optional): Retry attempts. Defaults to `0`. + """ + if not self.is_connected: + raise DeviceNotConnectedError( + f"{self.__class__.__name__}('{self.port}') is not connected. You need to run `{self.__class__.__name__}.connect()`." + ) + + id_ = self.motors[motor].id + model = self.motors[motor].model + addr, length = get_address(self.model_ctrl_table, model, data_name) + + if normalize and data_name in self.normalized_data: + value = self._unnormalize({id_: value})[id_] + + value = self._encode_sign(data_name, {id_: value})[id_] + + err_msg = f"Failed to write '{data_name}' on {id_=} with '{value}' after {num_retry + 1} tries." + self._write(addr, length, id_, value, num_retry=num_retry, raise_on_error=True, err_msg=err_msg) + + def _write( + self, + addr: int, + length: int, + motor_id: int, + value: int, + *, + num_retry: int = 0, + raise_on_error: bool = True, + err_msg: str = "", + ) -> tuple[int, int]: + data = self._serialize_data(value, length) + for n_try in range(1 + num_retry): + comm, error = self.packet_handler.writeTxRx(self.port_handler, motor_id, addr, length, data) + if self._is_comm_success(comm): + break + logger.debug( + f"Failed to sync write @{addr=} ({length=}) on id={motor_id} with {value=} ({n_try=}): " + + self.packet_handler.getTxRxResult(comm) + ) + + if not self._is_comm_success(comm) and raise_on_error: + raise ConnectionError(f"{err_msg} {self.packet_handler.getTxRxResult(comm)}") + elif self._is_error(error) and raise_on_error: + raise RuntimeError(f"{err_msg} {self.packet_handler.getRxPacketError(error)}") + + return comm, error + + def sync_read( + self, + data_name: str, + motors: str | list[str] | None = None, + *, + normalize: bool = True, + num_retry: int = 0, + ) -> dict[str, Value]: + """Read the same register from several motors at once. + + Args: + data_name (str): Register name. + motors (str | list[str] | None, optional): Motors to query. `None` (default) reads every motor. + normalize (bool, optional): Normalisation flag. Defaults to `True`. + num_retry (int, optional): Retry attempts. Defaults to `0`. + + Returns: + dict[str, Value]: Mapping *motor name → value*. + """ + if not self.is_connected: + raise DeviceNotConnectedError( + f"{self.__class__.__name__}('{self.port}') is not connected. You need to run `{self.__class__.__name__}.connect()`." + ) + + self._assert_protocol_is_compatible("sync_read") + + names = self._get_motors_list(motors) + ids = [self.motors[motor].id for motor in names] + models = [self.motors[motor].model for motor in names] + + if self._has_different_ctrl_tables: + assert_same_address(self.model_ctrl_table, models, data_name) + + model = next(iter(models)) + addr, length = get_address(self.model_ctrl_table, model, data_name) + + err_msg = f"Failed to sync read '{data_name}' on {ids=} after {num_retry + 1} tries." + ids_values, _ = self._sync_read( + addr, length, ids, num_retry=num_retry, raise_on_error=True, err_msg=err_msg + ) + + ids_values = self._decode_sign(data_name, ids_values) + + if normalize and data_name in self.normalized_data: + ids_values = self._normalize(ids_values) + + return {self._id_to_name(id_): value for id_, value in ids_values.items()} + + def _sync_read( + self, + addr: int, + length: int, + motor_ids: list[int], + *, + num_retry: int = 0, + raise_on_error: bool = True, + err_msg: str = "", + ) -> tuple[dict[int, int], int]: + self._setup_sync_reader(motor_ids, addr, length) + for n_try in range(1 + num_retry): + comm = self.sync_reader.txRxPacket() + if self._is_comm_success(comm): + break + logger.debug( + f"Failed to sync read @{addr=} ({length=}) on {motor_ids=} ({n_try=}): " + + self.packet_handler.getTxRxResult(comm) + ) + + if not self._is_comm_success(comm) and raise_on_error: + raise ConnectionError(f"{err_msg} {self.packet_handler.getTxRxResult(comm)}") + + values = {id_: self.sync_reader.getData(id_, addr, length) for id_ in motor_ids} + return values, comm + + def _setup_sync_reader(self, motor_ids: list[int], addr: int, length: int) -> None: + self.sync_reader.clearParam() + self.sync_reader.start_address = addr + self.sync_reader.data_length = length + for id_ in motor_ids: + self.sync_reader.addParam(id_) + + # TODO(aliberts, pkooij): Implementing something like this could get even much faster read times if need be. + # Would have to handle the logic of checking if a packet has been sent previously though but doable. + # This could be at the cost of increase latency between the moment the data is produced by the motors and + # the moment it is used by a policy. + # def _async_read(self, motor_ids: list[int], address: int, length: int): + # if self.sync_reader.start_address != address or self.sync_reader.data_length != length or ...: + # self._setup_sync_reader(motor_ids, address, length) + # else: + # self.sync_reader.rxPacket() + # self.sync_reader.txPacket() + + # for id_ in motor_ids: + # value = self.sync_reader.getData(id_, address, length) + + def sync_write( + self, + data_name: str, + values: Value | dict[str, Value], + *, + normalize: bool = True, + num_retry: int = 0, + ) -> None: + """Write the same register on multiple motors. + + Contrary to :pymeth:`write`, this *does not* expects a response status packet emitted by the motor, which + can allow for lost packets. It is faster than :pymeth:`write` and should typically be used when + frequency matters and losing some packets is acceptable (e.g. teleoperation loops). + + Args: + data_name (str): Register name. + values (Value | dict[str, Value]): Either a single value (applied to every motor) or a mapping + *motor name → value*. + normalize (bool, optional): If `True` (default) convert values from the user range to raw units. + num_retry (int, optional): Retry attempts. Defaults to `0`. + """ + if not self.is_connected: + raise DeviceNotConnectedError( + f"{self.__class__.__name__}('{self.port}') is not connected. You need to run `{self.__class__.__name__}.connect()`." + ) + + ids_values = self._get_ids_values_dict(values) + models = [self._id_to_model(id_) for id_ in ids_values] + if self._has_different_ctrl_tables: + assert_same_address(self.model_ctrl_table, models, data_name) + + model = next(iter(models)) + addr, length = get_address(self.model_ctrl_table, model, data_name) + + if normalize and data_name in self.normalized_data: + ids_values = self._unnormalize(ids_values) + + ids_values = self._encode_sign(data_name, ids_values) + + err_msg = f"Failed to sync write '{data_name}' with {ids_values=} after {num_retry + 1} tries." + self._sync_write(addr, length, ids_values, num_retry=num_retry, raise_on_error=True, err_msg=err_msg) + + def _sync_write( + self, + addr: int, + length: int, + ids_values: dict[int, int], + num_retry: int = 0, + raise_on_error: bool = True, + err_msg: str = "", + ) -> int: + self._setup_sync_writer(ids_values, addr, length) + for n_try in range(1 + num_retry): + comm = self.sync_writer.txPacket() + if self._is_comm_success(comm): + break + logger.debug( + f"Failed to sync write @{addr=} ({length=}) with {ids_values=} ({n_try=}): " + + self.packet_handler.getTxRxResult(comm) + ) + + if not self._is_comm_success(comm) and raise_on_error: + raise ConnectionError(f"{err_msg} {self.packet_handler.getTxRxResult(comm)}") + + return comm + + def _setup_sync_writer(self, ids_values: dict[int, int], addr: int, length: int) -> None: + self.sync_writer.clearParam() + self.sync_writer.start_address = addr + self.sync_writer.data_length = length + for id_, value in ids_values.items(): + data = self._serialize_data(value, length) + self.sync_writer.addParam(id_, data) diff --git a/lerobot/common/optim/__init__.py b/lerobot/common/optim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1e95939c9a00c5f9775524142f6aeb5cb348fb5e --- /dev/null +++ b/lerobot/common/optim/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .optimizers import OptimizerConfig as OptimizerConfig diff --git a/lerobot/common/optim/factory.py b/lerobot/common/optim/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..f7d5d46b8593e31f829489876f5d5683d9682bf3 --- /dev/null +++ b/lerobot/common/optim/factory.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from torch.optim import Optimizer +from torch.optim.lr_scheduler import LRScheduler + +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.configs.train import TrainPipelineConfig + + +def make_optimizer_and_scheduler( + cfg: TrainPipelineConfig, policy: PreTrainedPolicy +) -> tuple[Optimizer, LRScheduler | None]: + """Generates the optimizer and scheduler based on configs. + + Args: + cfg (TrainPipelineConfig): The training config that contains optimizer and scheduler configs + policy (PreTrainedPolicy): The policy config from which parameters and presets must be taken from. + + Returns: + tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`. + """ + params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters() + optimizer = cfg.optimizer.build(params) + lr_scheduler = cfg.scheduler.build(optimizer, cfg.steps) if cfg.scheduler is not None else None + return optimizer, lr_scheduler diff --git a/lerobot/common/optim/optimizers.py b/lerobot/common/optim/optimizers.py new file mode 100644 index 0000000000000000000000000000000000000000..950b70560bb253eaaab8a44a410d813ee80a638a --- /dev/null +++ b/lerobot/common/optim/optimizers.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import abc +from dataclasses import asdict, dataclass, field +from pathlib import Path +from typing import Any + +import draccus +import torch +from safetensors.torch import load_file, save_file + +from lerobot.common.constants import ( + OPTIMIZER_PARAM_GROUPS, + OPTIMIZER_STATE, +) +from lerobot.common.datasets.utils import flatten_dict, unflatten_dict, write_json +from lerobot.common.utils.io_utils import deserialize_json_into_object + + +@dataclass +class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC): + lr: float + weight_decay: float + grad_clip_norm: float + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) + + @classmethod + def default_choice_name(cls) -> str | None: + return "adam" + + @abc.abstractmethod + def build(self) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]: + """ + Build the optimizer. It can be a single optimizer or a dictionary of optimizers. + NOTE: Multiple optimizers are useful when you have different models to optimize. + For example, you can have one optimizer for the policy and another one for the value function + in reinforcement learning settings. + + Returns: + The optimizer or a dictionary of optimizers. + """ + raise NotImplementedError + + +@OptimizerConfig.register_subclass("adam") +@dataclass +class AdamConfig(OptimizerConfig): + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0.0 + grad_clip_norm: float = 10.0 + + def build(self, params: dict) -> torch.optim.Optimizer: + kwargs = asdict(self) + kwargs.pop("grad_clip_norm") + return torch.optim.Adam(params, **kwargs) + + +@OptimizerConfig.register_subclass("adamw") +@dataclass +class AdamWConfig(OptimizerConfig): + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 1e-2 + grad_clip_norm: float = 10.0 + + def build(self, params: dict) -> torch.optim.Optimizer: + kwargs = asdict(self) + kwargs.pop("grad_clip_norm") + return torch.optim.AdamW(params, **kwargs) + + +@OptimizerConfig.register_subclass("sgd") +@dataclass +class SGDConfig(OptimizerConfig): + lr: float = 1e-3 + momentum: float = 0.0 + dampening: float = 0.0 + nesterov: bool = False + weight_decay: float = 0.0 + grad_clip_norm: float = 10.0 + + def build(self, params: dict) -> torch.optim.Optimizer: + kwargs = asdict(self) + kwargs.pop("grad_clip_norm") + return torch.optim.SGD(params, **kwargs) + + +@OptimizerConfig.register_subclass("multi_adam") +@dataclass +class MultiAdamConfig(OptimizerConfig): + """Configuration for multiple Adam optimizers with different parameter groups. + + This creates a dictionary of Adam optimizers, each with its own hyperparameters. + + Args: + lr: Default learning rate (used if not specified for a group) + weight_decay: Default weight decay (used if not specified for a group) + optimizer_groups: Dictionary mapping parameter group names to their hyperparameters + grad_clip_norm: Gradient clipping norm + """ + + lr: float = 1e-3 + weight_decay: float = 0.0 + grad_clip_norm: float = 10.0 + optimizer_groups: dict[str, dict[str, Any]] = field(default_factory=dict) + + def build(self, params_dict: dict[str, list]) -> dict[str, torch.optim.Optimizer]: + """Build multiple Adam optimizers. + + Args: + params_dict: Dictionary mapping parameter group names to lists of parameters + The keys should match the keys in optimizer_groups + + Returns: + Dictionary mapping parameter group names to their optimizers + """ + optimizers = {} + + for name, params in params_dict.items(): + # Get group-specific hyperparameters or use defaults + group_config = self.optimizer_groups.get(name, {}) + + # Create optimizer with merged parameters (defaults + group-specific) + optimizer_kwargs = { + "lr": group_config.get("lr", self.lr), + "betas": group_config.get("betas", (0.9, 0.999)), + "eps": group_config.get("eps", 1e-5), + "weight_decay": group_config.get("weight_decay", self.weight_decay), + } + + optimizers[name] = torch.optim.Adam(params, **optimizer_kwargs) + + return optimizers + + +def save_optimizer_state( + optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path +) -> None: + """Save optimizer state to disk. + + Args: + optimizer: Either a single optimizer or a dictionary of optimizers. + save_dir: Directory to save the optimizer state. + """ + if isinstance(optimizer, dict): + # Handle dictionary of optimizers + for name, opt in optimizer.items(): + optimizer_dir = save_dir / name + optimizer_dir.mkdir(exist_ok=True, parents=True) + _save_single_optimizer_state(opt, optimizer_dir) + else: + # Handle single optimizer + _save_single_optimizer_state(optimizer, save_dir) + + +def _save_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None: + """Save a single optimizer's state to disk.""" + state = optimizer.state_dict() + param_groups = state.pop("param_groups") + flat_state = flatten_dict(state) + save_file(flat_state, save_dir / OPTIMIZER_STATE) + write_json(param_groups, save_dir / OPTIMIZER_PARAM_GROUPS) + + +def load_optimizer_state( + optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path +) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]: + """Load optimizer state from disk. + + Args: + optimizer: Either a single optimizer or a dictionary of optimizers. + save_dir: Directory to load the optimizer state from. + + Returns: + The updated optimizer(s) with loaded state. + """ + if isinstance(optimizer, dict): + # Handle dictionary of optimizers + loaded_optimizers = {} + for name, opt in optimizer.items(): + optimizer_dir = save_dir / name + if optimizer_dir.exists(): + loaded_optimizers[name] = _load_single_optimizer_state(opt, optimizer_dir) + else: + loaded_optimizers[name] = opt + return loaded_optimizers + else: + # Handle single optimizer + return _load_single_optimizer_state(optimizer, save_dir) + + +def _load_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> torch.optim.Optimizer: + """Load a single optimizer's state from disk.""" + current_state_dict = optimizer.state_dict() + flat_state = load_file(save_dir / OPTIMIZER_STATE) + state = unflatten_dict(flat_state) + + # Handle case where 'state' key might not exist (for newly created optimizers) + if "state" in state: + loaded_state_dict = {"state": {int(k): v for k, v in state["state"].items()}} + else: + loaded_state_dict = {"state": {}} + + if "param_groups" in current_state_dict: + param_groups = deserialize_json_into_object( + save_dir / OPTIMIZER_PARAM_GROUPS, current_state_dict["param_groups"] + ) + loaded_state_dict["param_groups"] = param_groups + + optimizer.load_state_dict(loaded_state_dict) + return optimizer diff --git a/lerobot/common/optim/schedulers.py b/lerobot/common/optim/schedulers.py new file mode 100644 index 0000000000000000000000000000000000000000..3610bf4a27f45cc1ca47c54315fd3f66d1b5f942 --- /dev/null +++ b/lerobot/common/optim/schedulers.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import abc +import math +from dataclasses import asdict, dataclass +from pathlib import Path + +import draccus +from torch.optim import Optimizer +from torch.optim.lr_scheduler import LambdaLR, LRScheduler + +from lerobot.common.constants import SCHEDULER_STATE +from lerobot.common.datasets.utils import write_json +from lerobot.common.utils.io_utils import deserialize_json_into_object + + +@dataclass +class LRSchedulerConfig(draccus.ChoiceRegistry, abc.ABC): + num_warmup_steps: int + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) + + @abc.abstractmethod + def build(self, optimizer: Optimizer, num_training_steps: int) -> LRScheduler | None: + raise NotImplementedError + + +@LRSchedulerConfig.register_subclass("diffuser") +@dataclass +class DiffuserSchedulerConfig(LRSchedulerConfig): + name: str = "cosine" + num_warmup_steps: int | None = None + + def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR: + from diffusers.optimization import get_scheduler + + kwargs = {**asdict(self), "num_training_steps": num_training_steps, "optimizer": optimizer} + return get_scheduler(**kwargs) + + +@LRSchedulerConfig.register_subclass("vqbet") +@dataclass +class VQBeTSchedulerConfig(LRSchedulerConfig): + num_warmup_steps: int + num_vqvae_training_steps: int + num_cycles: float = 0.5 + + def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR: + def lr_lambda(current_step): + if current_step < self.num_vqvae_training_steps: + return float(1) + else: + adjusted_step = current_step - self.num_vqvae_training_steps + if adjusted_step < self.num_warmup_steps: + return float(adjusted_step) / float(max(1, self.num_warmup_steps)) + progress = float(adjusted_step - self.num_warmup_steps) / float( + max(1, num_training_steps - self.num_warmup_steps) + ) + return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.num_cycles) * 2.0 * progress))) + + return LambdaLR(optimizer, lr_lambda, -1) + + +@LRSchedulerConfig.register_subclass("cosine_decay_with_warmup") +@dataclass +class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig): + """Used by Physical Intelligence to train Pi0""" + + num_warmup_steps: int + num_decay_steps: int + peak_lr: float + decay_lr: float + + def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR: + del num_training_steps + + def lr_lambda(current_step): + def linear_warmup_schedule(current_step): + if current_step <= 0: + return 1 / (self.num_warmup_steps + 1) + frac = 1 - current_step / self.num_warmup_steps + return (1 / (self.num_warmup_steps + 1) - 1) * frac + 1 + + def cosine_decay_schedule(current_step): + step = min(current_step, self.num_decay_steps) + cosine_decay = 0.5 * (1 + math.cos(math.pi * step / self.num_decay_steps)) + alpha = self.decay_lr / self.peak_lr + decayed = (1 - alpha) * cosine_decay + alpha + return decayed + + if current_step < self.num_warmup_steps: + return linear_warmup_schedule(current_step) + + return cosine_decay_schedule(current_step) + + return LambdaLR(optimizer, lr_lambda, -1) + + +def save_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> None: + state_dict = scheduler.state_dict() + write_json(state_dict, save_dir / SCHEDULER_STATE) + + +def load_scheduler_state(scheduler: LRScheduler, save_dir: Path) -> LRScheduler: + state_dict = deserialize_json_into_object(save_dir / SCHEDULER_STATE, scheduler.state_dict()) + scheduler.load_state_dict(state_dict) + return scheduler diff --git a/lerobot/common/policies/__init__.py b/lerobot/common/policies/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0500fad3c1264aa0f06988974576d4f6227523a7 --- /dev/null +++ b/lerobot/common/policies/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .act.configuration_act import ACTConfig as ACTConfig +from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig +from .pi0.configuration_pi0 import PI0Config as PI0Config +from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig +from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig +from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig diff --git a/lerobot/common/policies/act/configuration_act.py b/lerobot/common/policies/act/configuration_act.py new file mode 100644 index 0000000000000000000000000000000000000000..5e3fcd77ece896062758cc266bafcf2fc5adf2db --- /dev/null +++ b/lerobot/common/policies/act/configuration_act.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python + +# Copyright 2024 Tony Z. Zhao and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamWConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import NormalizationMode + + +@PreTrainedConfig.register_subclass("act") +@dataclass +class ACTConfig(PreTrainedConfig): + """Configuration class for the Action Chunking Transformers policy. + + Defaults are configured for training on bimanual Aloha tasks like "insertion" or "transfer". + + The parameters you will most likely need to change are the ones which depend on the environment / sensors. + Those are: `input_shapes` and 'output_shapes`. + + Notes on the inputs and outputs: + - Either: + - At least one key starting with "observation.image is required as an input. + AND/OR + - The key "observation.environment_state" is required as input. + - If there are multiple keys beginning with "observation.images." they are treated as multiple camera + views. Right now we only support all images having the same shape. + - May optionally work without an "observation.state" key for the proprioceptive robot state. + - "action" is required as an output key. + + Args: + n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the + current step and additional steps going back). + chunk_size: The size of the action prediction "chunks" in units of environment steps. + n_action_steps: The number of action steps to run in the environment for one invocation of the policy. + This should be no greater than the chunk size. For example, if the chunk size size 100, you may + set this to 50. This would mean that the model predicts 100 steps worth of actions, runs 50 in the + environment, and throws the other 50 out. + input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents + the input data name, and the value is a list indicating the dimensions of the corresponding data. + For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], + indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't + include batch dimension or temporal dimension. + output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents + the output data name, and the value is a list indicating the dimensions of the corresponding data. + For example, "action" refers to an output shape of [14], indicating 14-dimensional actions. + Importantly, `output_shapes` doesn't include batch dimension or temporal dimension. + input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), + and the value specifies the normalization mode to apply. The two available modes are "mean_std" + which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a + [-1, 1] range. + output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the + original scale. Note that this is also used for normalizing the training targets. + vision_backbone: Name of the torchvision resnet backbone to use for encoding images. + pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone. + `None` means no pretrained weights. + replace_final_stride_with_dilation: Whether to replace the ResNet's final 2x2 stride with a dilated + convolution. + pre_norm: Whether to use "pre-norm" in the transformer blocks. + dim_model: The transformer blocks' main hidden dimension. + n_heads: The number of heads to use in the transformer blocks' multi-head attention. + dim_feedforward: The dimension to expand the transformer's hidden dimension to in the feed-forward + layers. + feedforward_activation: The activation to use in the transformer block's feed-forward layers. + n_encoder_layers: The number of transformer layers to use for the transformer encoder. + n_decoder_layers: The number of transformer layers to use for the transformer decoder. + use_vae: Whether to use a variational objective during training. This introduces another transformer + which is used as the VAE's encoder (not to be confused with the transformer encoder - see + documentation in the policy class). + latent_dim: The VAE's latent dimension. + n_vae_encoder_layers: The number of transformer layers to use for the VAE's encoder. + temporal_ensemble_coeff: Coefficient for the exponential weighting scheme to apply for temporal + ensembling. Defaults to None which means temporal ensembling is not used. `n_action_steps` must be + 1 when using this feature, as inference needs to happen at every step to form an ensemble. For + more information on how ensembling works, please see `ACTTemporalEnsembler`. + dropout: Dropout to use in the transformer layers (see code for details). + kl_weight: The weight to use for the KL-divergence component of the loss if the variational objective + is enabled. Loss is then calculated as: `reconstruction_loss + kl_weight * kld_loss`. + """ + + # Input / output structure. + n_obs_steps: int = 1 + chunk_size: int = 100 + n_action_steps: int = 100 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.MEAN_STD, + "STATE": NormalizationMode.MEAN_STD, + "ACTION": NormalizationMode.MEAN_STD, + } + ) + + # Architecture. + # Vision backbone. + vision_backbone: str = "resnet18" + pretrained_backbone_weights: str | None = "ResNet18_Weights.IMAGENET1K_V1" + replace_final_stride_with_dilation: int = False + # Transformer layers. + pre_norm: bool = False + dim_model: int = 512 + n_heads: int = 8 + dim_feedforward: int = 3200 + feedforward_activation: str = "relu" + n_encoder_layers: int = 4 + # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code + # that means only the first layer is used. Here we match the original implementation by setting this to 1. + # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521. + n_decoder_layers: int = 1 + # VAE. + use_vae: bool = True + latent_dim: int = 32 + n_vae_encoder_layers: int = 4 + + # Inference. + # Note: the value used in ACT when temporal ensembling is enabled is 0.01. + temporal_ensemble_coeff: float | None = None + + # Training and loss computation. + dropout: float = 0.1 + kl_weight: float = 10.0 + + # Training preset + optimizer_lr: float = 1e-5 + optimizer_weight_decay: float = 1e-4 + optimizer_lr_backbone: float = 1e-5 + + def __post_init__(self): + super().__post_init__() + + """Input validation (not exhaustive).""" + if not self.vision_backbone.startswith("resnet"): + raise ValueError( + f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}." + ) + if self.temporal_ensemble_coeff is not None and self.n_action_steps > 1: + raise NotImplementedError( + "`n_action_steps` must be 1 when using temporal ensembling. This is " + "because the policy needs to be queried every step to compute the ensembled action." + ) + if self.n_action_steps > self.chunk_size: + raise ValueError( + f"The chunk size is the upper bound for the number of action steps per model invocation. Got " + f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`." + ) + if self.n_obs_steps != 1: + raise ValueError( + f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`" + ) + + def get_optimizer_preset(self) -> AdamWConfig: + return AdamWConfig( + lr=self.optimizer_lr, + weight_decay=self.optimizer_weight_decay, + ) + + def get_scheduler_preset(self) -> None: + return None + + def validate_features(self) -> None: + if not self.image_features and not self.env_state_feature: + raise ValueError("You must provide at least one image or the environment state among the inputs.") + + @property + def observation_delta_indices(self) -> None: + return None + + @property + def action_delta_indices(self) -> list: + return list(range(self.chunk_size)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/act/modeling_act.py b/lerobot/common/policies/act/modeling_act.py new file mode 100644 index 0000000000000000000000000000000000000000..0fc5b4e6a74265f5cf66dc63d2af1f9e705adeb8 --- /dev/null +++ b/lerobot/common/policies/act/modeling_act.py @@ -0,0 +1,765 @@ +#!/usr/bin/env python + +# Copyright 2024 Tony Z. Zhao and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Action Chunking Transformer Policy + +As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://huggingface.co/papers/2304.13705). +The majority of changes here involve removing unused code, unifying naming, and adding helpful comments. +""" + +import math +from collections import deque +from itertools import chain +from typing import Callable + +import einops +import numpy as np +import torch +import torch.nn.functional as F # noqa: N812 +import torchvision +from torch import Tensor, nn +from torchvision.models._utils import IntermediateLayerGetter +from torchvision.ops.misc import FrozenBatchNorm2d + +from lerobot.common.policies.act.configuration_act import ACTConfig +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pretrained import PreTrainedPolicy + + +class ACTPolicy(PreTrainedPolicy): + """ + Action Chunking Transformer Policy as per Learning Fine-Grained Bimanual Manipulation with Low-Cost + Hardware (paper: https://huggingface.co/papers/2304.13705, code: https://github.com/tonyzhaozh/act) + """ + + config_class = ACTConfig + name = "act" + + def __init__( + self, + config: ACTConfig, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + super().__init__(config) + config.validate_features() + self.config = config + + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + self.model = ACT(config) + + if config.temporal_ensemble_coeff is not None: + self.temporal_ensembler = ACTTemporalEnsembler(config.temporal_ensemble_coeff, config.chunk_size) + + self.reset() + + def get_optim_params(self) -> dict: + # TODO(aliberts, rcadene): As of now, lr_backbone == lr + # Should we remove this and just `return self.parameters()`? + return [ + { + "params": [ + p + for n, p in self.named_parameters() + if not n.startswith("model.backbone") and p.requires_grad + ] + }, + { + "params": [ + p + for n, p in self.named_parameters() + if n.startswith("model.backbone") and p.requires_grad + ], + "lr": self.config.optimizer_lr_backbone, + }, + ] + + def reset(self): + """This should be called whenever the environment is reset.""" + if self.config.temporal_ensemble_coeff is not None: + self.temporal_ensembler.reset() + else: + self._action_queue = deque([], maxlen=self.config.n_action_steps) + + @torch.no_grad + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations. + + This method wraps `select_actions` in order to return one action at a time for execution in the + environment. It works by managing the actions in a queue and only calling `select_actions` when the + queue is empty. + """ + self.eval() + + batch = self.normalize_inputs(batch) + if self.config.image_features: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.images"] = [batch[key] for key in self.config.image_features] + + # If we are doing temporal ensembling, do online updates where we keep track of the number of actions + # we are ensembling over. + if self.config.temporal_ensemble_coeff is not None: + actions = self.model(batch)[0] # (batch_size, chunk_size, action_dim) + actions = self.unnormalize_outputs({"action": actions})["action"] + action = self.temporal_ensembler.update(actions) + return action + + # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by + # querying the policy. + if len(self._action_queue) == 0: + actions = self.model(batch)[0][:, : self.config.n_action_steps] + + # TODO(rcadene): make _forward return output dictionary? + actions = self.unnormalize_outputs({"action": actions})["action"] + + # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue + # effectively has shape (n_action_steps, batch_size, *), hence the transpose. + self._action_queue.extend(actions.transpose(0, 1)) + return self._action_queue.popleft() + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: + """Run the batch through the model and compute the loss for training or validation.""" + batch = self.normalize_inputs(batch) + if self.config.image_features: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.images"] = [batch[key] for key in self.config.image_features] + + batch = self.normalize_targets(batch) + actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch) + + l1_loss = ( + F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1) + ).mean() + + loss_dict = {"l1_loss": l1_loss.item()} + if self.config.use_vae: + # Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for + # each dimension independently, we sum over the latent dimension to get the total + # KL-divergence per batch element, then take the mean over the batch. + # (See App. B of https://huggingface.co/papers/1312.6114 for more details). + mean_kld = ( + (-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean() + ) + loss_dict["kld_loss"] = mean_kld.item() + loss = l1_loss + mean_kld * self.config.kl_weight + else: + loss = l1_loss + + return loss, loss_dict + + +class ACTTemporalEnsembler: + def __init__(self, temporal_ensemble_coeff: float, chunk_size: int) -> None: + """Temporal ensembling as described in Algorithm 2 of https://huggingface.co/papers/2304.13705. + + The weights are calculated as wᵢ = exp(-temporal_ensemble_coeff * i) where w₀ is the oldest action. + They are then normalized to sum to 1 by dividing by Σwᵢ. Here's some intuition around how the + coefficient works: + - Setting it to 0 uniformly weighs all actions. + - Setting it positive gives more weight to older actions. + - Setting it negative gives more weight to newer actions. + NOTE: The default value for `temporal_ensemble_coeff` used by the original ACT work is 0.01. This + results in older actions being weighed more highly than newer actions (the experiments documented in + https://github.com/huggingface/lerobot/pull/319 hint at why highly weighing new actions might be + detrimental: doing so aggressively may diminish the benefits of action chunking). + + Here we use an online method for computing the average rather than caching a history of actions in + order to compute the average offline. For a simple 1D sequence it looks something like: + + ``` + import torch + + seq = torch.linspace(8, 8.5, 100) + print(seq) + + m = 0.01 + exp_weights = torch.exp(-m * torch.arange(len(seq))) + print(exp_weights) + + # Calculate offline + avg = (exp_weights * seq).sum() / exp_weights.sum() + print("offline", avg) + + # Calculate online + for i, item in enumerate(seq): + if i == 0: + avg = item + continue + avg *= exp_weights[:i].sum() + avg += item * exp_weights[i] + avg /= exp_weights[:i+1].sum() + print("online", avg) + ``` + """ + self.chunk_size = chunk_size + self.ensemble_weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size)) + self.ensemble_weights_cumsum = torch.cumsum(self.ensemble_weights, dim=0) + self.reset() + + def reset(self): + """Resets the online computation variables.""" + self.ensembled_actions = None + # (chunk_size,) count of how many actions are in the ensemble for each time step in the sequence. + self.ensembled_actions_count = None + + def update(self, actions: Tensor) -> Tensor: + """ + Takes a (batch, chunk_size, action_dim) sequence of actions, update the temporal ensemble for all + time steps, and pop/return the next batch of actions in the sequence. + """ + self.ensemble_weights = self.ensemble_weights.to(device=actions.device) + self.ensemble_weights_cumsum = self.ensemble_weights_cumsum.to(device=actions.device) + if self.ensembled_actions is None: + # Initializes `self._ensembled_action` to the sequence of actions predicted during the first + # time step of the episode. + self.ensembled_actions = actions.clone() + # Note: The last dimension is unsqueeze to make sure we can broadcast properly for tensor + # operations later. + self.ensembled_actions_count = torch.ones( + (self.chunk_size, 1), dtype=torch.long, device=self.ensembled_actions.device + ) + else: + # self.ensembled_actions will have shape (batch_size, chunk_size - 1, action_dim). Compute + # the online update for those entries. + self.ensembled_actions *= self.ensemble_weights_cumsum[self.ensembled_actions_count - 1] + self.ensembled_actions += actions[:, :-1] * self.ensemble_weights[self.ensembled_actions_count] + self.ensembled_actions /= self.ensemble_weights_cumsum[self.ensembled_actions_count] + self.ensembled_actions_count = torch.clamp(self.ensembled_actions_count + 1, max=self.chunk_size) + # The last action, which has no prior online average, needs to get concatenated onto the end. + self.ensembled_actions = torch.cat([self.ensembled_actions, actions[:, -1:]], dim=1) + self.ensembled_actions_count = torch.cat( + [self.ensembled_actions_count, torch.ones_like(self.ensembled_actions_count[-1:])] + ) + # "Consume" the first action. + action, self.ensembled_actions, self.ensembled_actions_count = ( + self.ensembled_actions[:, 0], + self.ensembled_actions[:, 1:], + self.ensembled_actions_count[1:], + ) + return action + + +class ACT(nn.Module): + """Action Chunking Transformer: The underlying neural network for ACTPolicy. + + Note: In this code we use the terms `vae_encoder`, 'encoder', `decoder`. The meanings are as follows. + - The `vae_encoder` is, as per the literature around variational auto-encoders (VAE), the part of the + model that encodes the target data (a sequence of actions), and the condition (the robot + joint-space). + - A transformer with an `encoder` (not the VAE encoder) and `decoder` (not the VAE decoder) with + cross-attention is used as the VAE decoder. For these terms, we drop the `vae_` prefix because we + have an option to train this model without the variational objective (in which case we drop the + `vae_encoder` altogether, and nothing about this model has anything to do with a VAE). + + Transformer + Used alone for inference + (acts as VAE decoder + during training) + ┌───────────────────────┐ + │ Outputs │ + │ ▲ │ + │ ┌─────►┌───────┐ │ + ┌──────┐ │ │ │Transf.│ │ + │ │ │ ├─────►│decoder│ │ + ┌────┴────┐ │ │ │ │ │ │ + │ │ │ │ ┌───┴───┬─►│ │ │ + │ VAE │ │ │ │ │ └───────┘ │ + │ encoder │ │ │ │Transf.│ │ + │ │ │ │ │encoder│ │ + └───▲─────┘ │ │ │ │ │ + │ │ │ └▲──▲─▲─┘ │ + │ │ │ │ │ │ │ + inputs └─────┼──┘ │ image emb. │ + │ state emb. │ + └───────────────────────┘ + """ + + def __init__(self, config: ACTConfig): + # BERT style VAE encoder with input tokens [cls, robot_state, *action_sequence]. + # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]). + super().__init__() + self.config = config + + if self.config.use_vae: + self.vae_encoder = ACTEncoder(config, is_vae_encoder=True) + self.vae_encoder_cls_embed = nn.Embedding(1, config.dim_model) + # Projection layer for joint-space configuration to hidden dimension. + if self.config.robot_state_feature: + self.vae_encoder_robot_state_input_proj = nn.Linear( + self.config.robot_state_feature.shape[0], config.dim_model + ) + # Projection layer for action (joint-space target) to hidden dimension. + self.vae_encoder_action_input_proj = nn.Linear( + self.config.action_feature.shape[0], + config.dim_model, + ) + # Projection layer from the VAE encoder's output to the latent distribution's parameter space. + self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, config.latent_dim * 2) + # Fixed sinusoidal positional embedding for the input to the VAE encoder. Unsqueeze for batch + # dimension. + num_input_token_encoder = 1 + config.chunk_size + if self.config.robot_state_feature: + num_input_token_encoder += 1 + self.register_buffer( + "vae_encoder_pos_enc", + create_sinusoidal_pos_embedding(num_input_token_encoder, config.dim_model).unsqueeze(0), + ) + + # Backbone for image feature extraction. + if self.config.image_features: + backbone_model = getattr(torchvision.models, config.vision_backbone)( + replace_stride_with_dilation=[False, False, config.replace_final_stride_with_dilation], + weights=config.pretrained_backbone_weights, + norm_layer=FrozenBatchNorm2d, + ) + # Note: The assumption here is that we are using a ResNet model (and hence layer4 is the final + # feature map). + # Note: The forward method of this returns a dict: {"feature_map": output}. + self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"}) + + # Transformer (acts as VAE decoder when training with the variational objective). + self.encoder = ACTEncoder(config) + self.decoder = ACTDecoder(config) + + # Transformer encoder input projections. The tokens will be structured like + # [latent, (robot_state), (env_state), (image_feature_map_pixels)]. + if self.config.robot_state_feature: + self.encoder_robot_state_input_proj = nn.Linear( + self.config.robot_state_feature.shape[0], config.dim_model + ) + if self.config.env_state_feature: + self.encoder_env_state_input_proj = nn.Linear( + self.config.env_state_feature.shape[0], config.dim_model + ) + self.encoder_latent_input_proj = nn.Linear(config.latent_dim, config.dim_model) + if self.config.image_features: + self.encoder_img_feat_input_proj = nn.Conv2d( + backbone_model.fc.in_features, config.dim_model, kernel_size=1 + ) + # Transformer encoder positional embeddings. + n_1d_tokens = 1 # for the latent + if self.config.robot_state_feature: + n_1d_tokens += 1 + if self.config.env_state_feature: + n_1d_tokens += 1 + self.encoder_1d_feature_pos_embed = nn.Embedding(n_1d_tokens, config.dim_model) + if self.config.image_features: + self.encoder_cam_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2) + + # Transformer decoder. + # Learnable positional embedding for the transformer's decoder (in the style of DETR object queries). + self.decoder_pos_embed = nn.Embedding(config.chunk_size, config.dim_model) + + # Final action regression head on the output of the transformer's decoder. + self.action_head = nn.Linear(config.dim_model, self.config.action_feature.shape[0]) + + self._reset_parameters() + + def _reset_parameters(self): + """Xavier-uniform initialization of the transformer parameters as in the original code.""" + for p in chain(self.encoder.parameters(), self.decoder.parameters()): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tensor] | tuple[None, None]]: + """A forward pass through the Action Chunking Transformer (with optional VAE encoder). + + `batch` should have the following structure: + { + [robot_state_feature] (optional): (B, state_dim) batch of robot states. + + [image_features]: (B, n_cameras, C, H, W) batch of images. + AND/OR + [env_state_feature]: (B, env_dim) batch of environment states. + + [action_feature] (optional, only if training with VAE): (B, chunk_size, action dim) batch of actions. + } + + Returns: + (B, chunk_size, action_dim) batch of action sequences + Tuple containing the latent PDF's parameters (mean, log(σ²)) both as (B, L) tensors where L is the + latent dimension. + """ + if self.config.use_vae and self.training: + assert "action" in batch, ( + "actions must be provided when using the variational objective in training mode." + ) + + if "observation.images" in batch: + batch_size = batch["observation.images"][0].shape[0] + else: + batch_size = batch["observation.environment_state"].shape[0] + + # Prepare the latent for input to the transformer encoder. + if self.config.use_vae and "action" in batch: + # Prepare the input to the VAE encoder: [cls, *joint_space_configuration, *action_sequence]. + cls_embed = einops.repeat( + self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size + ) # (B, 1, D) + if self.config.robot_state_feature: + robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"]) + robot_state_embed = robot_state_embed.unsqueeze(1) # (B, 1, D) + action_embed = self.vae_encoder_action_input_proj(batch["action"]) # (B, S, D) + + if self.config.robot_state_feature: + vae_encoder_input = [cls_embed, robot_state_embed, action_embed] # (B, S+2, D) + else: + vae_encoder_input = [cls_embed, action_embed] + vae_encoder_input = torch.cat(vae_encoder_input, axis=1) + + # Prepare fixed positional embedding. + # Note: detach() shouldn't be necessary but leaving it the same as the original code just in case. + pos_embed = self.vae_encoder_pos_enc.clone().detach() # (1, S+2, D) + + # Prepare key padding mask for the transformer encoder. We have 1 or 2 extra tokens at the start of the + # sequence depending whether we use the input states or not (cls and robot state) + # False means not a padding token. + cls_joint_is_pad = torch.full( + (batch_size, 2 if self.config.robot_state_feature else 1), + False, + device=batch["observation.state"].device, + ) + key_padding_mask = torch.cat( + [cls_joint_is_pad, batch["action_is_pad"]], axis=1 + ) # (bs, seq+1 or 2) + + # Forward pass through VAE encoder to get the latent PDF parameters. + cls_token_out = self.vae_encoder( + vae_encoder_input.permute(1, 0, 2), + pos_embed=pos_embed.permute(1, 0, 2), + key_padding_mask=key_padding_mask, + )[0] # select the class token, with shape (B, D) + latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out) + mu = latent_pdf_params[:, : self.config.latent_dim] + # This is 2log(sigma). Done this way to match the original implementation. + log_sigma_x2 = latent_pdf_params[:, self.config.latent_dim :] + + # Sample the latent with the reparameterization trick. + latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu) + else: + # When not using the VAE encoder, we set the latent to be all zeros. + mu = log_sigma_x2 = None + # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use buffer + latent_sample = torch.zeros([batch_size, self.config.latent_dim], dtype=torch.float32).to( + batch["observation.state"].device + ) + + # Prepare transformer encoder inputs. + encoder_in_tokens = [self.encoder_latent_input_proj(latent_sample)] + encoder_in_pos_embed = list(self.encoder_1d_feature_pos_embed.weight.unsqueeze(1)) + # Robot state token. + if self.config.robot_state_feature: + encoder_in_tokens.append(self.encoder_robot_state_input_proj(batch["observation.state"])) + # Environment state token. + if self.config.env_state_feature: + encoder_in_tokens.append( + self.encoder_env_state_input_proj(batch["observation.environment_state"]) + ) + + # Camera observation features and positional embeddings. + if self.config.image_features: + all_cam_features = [] + all_cam_pos_embeds = [] + + # For a list of images, the H and W may vary but H*W is constant. + for img in batch["observation.images"]: + cam_features = self.backbone(img)["feature_map"] + cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype) + cam_features = self.encoder_img_feat_input_proj(cam_features) + + # Rearrange features to (sequence, batch, dim). + cam_features = einops.rearrange(cam_features, "b c h w -> (h w) b c") + cam_pos_embed = einops.rearrange(cam_pos_embed, "b c h w -> (h w) b c") + + all_cam_features.append(cam_features) + all_cam_pos_embeds.append(cam_pos_embed) + + encoder_in_tokens.extend(torch.cat(all_cam_features, axis=0)) + encoder_in_pos_embed.extend(torch.cat(all_cam_pos_embeds, axis=0)) + + # Stack all tokens along the sequence dimension. + encoder_in_tokens = torch.stack(encoder_in_tokens, axis=0) + encoder_in_pos_embed = torch.stack(encoder_in_pos_embed, axis=0) + + # Forward pass through the transformer modules. + encoder_out = self.encoder(encoder_in_tokens, pos_embed=encoder_in_pos_embed) + # TODO(rcadene, alexander-soare): remove call to `device` ; precompute and use buffer + decoder_in = torch.zeros( + (self.config.chunk_size, batch_size, self.config.dim_model), + dtype=encoder_in_pos_embed.dtype, + device=encoder_in_pos_embed.device, + ) + decoder_out = self.decoder( + decoder_in, + encoder_out, + encoder_pos_embed=encoder_in_pos_embed, + decoder_pos_embed=self.decoder_pos_embed.weight.unsqueeze(1), + ) + + # Move back to (B, S, C). + decoder_out = decoder_out.transpose(0, 1) + + actions = self.action_head(decoder_out) + + return actions, (mu, log_sigma_x2) + + +class ACTEncoder(nn.Module): + """Convenience module for running multiple encoder layers, maybe followed by normalization.""" + + def __init__(self, config: ACTConfig, is_vae_encoder: bool = False): + super().__init__() + self.is_vae_encoder = is_vae_encoder + num_layers = config.n_vae_encoder_layers if self.is_vae_encoder else config.n_encoder_layers + self.layers = nn.ModuleList([ACTEncoderLayer(config) for _ in range(num_layers)]) + self.norm = nn.LayerNorm(config.dim_model) if config.pre_norm else nn.Identity() + + def forward( + self, x: Tensor, pos_embed: Tensor | None = None, key_padding_mask: Tensor | None = None + ) -> Tensor: + for layer in self.layers: + x = layer(x, pos_embed=pos_embed, key_padding_mask=key_padding_mask) + x = self.norm(x) + return x + + +class ACTEncoderLayer(nn.Module): + def __init__(self, config: ACTConfig): + super().__init__() + self.self_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout) + + # Feed forward layers. + self.linear1 = nn.Linear(config.dim_model, config.dim_feedforward) + self.dropout = nn.Dropout(config.dropout) + self.linear2 = nn.Linear(config.dim_feedforward, config.dim_model) + + self.norm1 = nn.LayerNorm(config.dim_model) + self.norm2 = nn.LayerNorm(config.dim_model) + self.dropout1 = nn.Dropout(config.dropout) + self.dropout2 = nn.Dropout(config.dropout) + + self.activation = get_activation_fn(config.feedforward_activation) + self.pre_norm = config.pre_norm + + def forward(self, x, pos_embed: Tensor | None = None, key_padding_mask: Tensor | None = None) -> Tensor: + skip = x + if self.pre_norm: + x = self.norm1(x) + q = k = x if pos_embed is None else x + pos_embed + x = self.self_attn(q, k, value=x, key_padding_mask=key_padding_mask) + x = x[0] # note: [0] to select just the output, not the attention weights + x = skip + self.dropout1(x) + if self.pre_norm: + skip = x + x = self.norm2(x) + else: + x = self.norm1(x) + skip = x + x = self.linear2(self.dropout(self.activation(self.linear1(x)))) + x = skip + self.dropout2(x) + if not self.pre_norm: + x = self.norm2(x) + return x + + +class ACTDecoder(nn.Module): + def __init__(self, config: ACTConfig): + """Convenience module for running multiple decoder layers followed by normalization.""" + super().__init__() + self.layers = nn.ModuleList([ACTDecoderLayer(config) for _ in range(config.n_decoder_layers)]) + self.norm = nn.LayerNorm(config.dim_model) + + def forward( + self, + x: Tensor, + encoder_out: Tensor, + decoder_pos_embed: Tensor | None = None, + encoder_pos_embed: Tensor | None = None, + ) -> Tensor: + for layer in self.layers: + x = layer( + x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed + ) + if self.norm is not None: + x = self.norm(x) + return x + + +class ACTDecoderLayer(nn.Module): + def __init__(self, config: ACTConfig): + super().__init__() + self.self_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout) + self.multihead_attn = nn.MultiheadAttention(config.dim_model, config.n_heads, dropout=config.dropout) + + # Feed forward layers. + self.linear1 = nn.Linear(config.dim_model, config.dim_feedforward) + self.dropout = nn.Dropout(config.dropout) + self.linear2 = nn.Linear(config.dim_feedforward, config.dim_model) + + self.norm1 = nn.LayerNorm(config.dim_model) + self.norm2 = nn.LayerNorm(config.dim_model) + self.norm3 = nn.LayerNorm(config.dim_model) + self.dropout1 = nn.Dropout(config.dropout) + self.dropout2 = nn.Dropout(config.dropout) + self.dropout3 = nn.Dropout(config.dropout) + + self.activation = get_activation_fn(config.feedforward_activation) + self.pre_norm = config.pre_norm + + def maybe_add_pos_embed(self, tensor: Tensor, pos_embed: Tensor | None) -> Tensor: + return tensor if pos_embed is None else tensor + pos_embed + + def forward( + self, + x: Tensor, + encoder_out: Tensor, + decoder_pos_embed: Tensor | None = None, + encoder_pos_embed: Tensor | None = None, + ) -> Tensor: + """ + Args: + x: (Decoder Sequence, Batch, Channel) tensor of input tokens. + encoder_out: (Encoder Sequence, B, C) output features from the last layer of the encoder we are + cross-attending with. + decoder_pos_embed: (ES, 1, C) positional embedding for keys (from the encoder). + encoder_pos_embed: (DS, 1, C) Positional_embedding for the queries (from the decoder). + Returns: + (DS, B, C) tensor of decoder output features. + """ + skip = x + if self.pre_norm: + x = self.norm1(x) + q = k = self.maybe_add_pos_embed(x, decoder_pos_embed) + x = self.self_attn(q, k, value=x)[0] # select just the output, not the attention weights + x = skip + self.dropout1(x) + if self.pre_norm: + skip = x + x = self.norm2(x) + else: + x = self.norm1(x) + skip = x + x = self.multihead_attn( + query=self.maybe_add_pos_embed(x, decoder_pos_embed), + key=self.maybe_add_pos_embed(encoder_out, encoder_pos_embed), + value=encoder_out, + )[0] # select just the output, not the attention weights + x = skip + self.dropout2(x) + if self.pre_norm: + skip = x + x = self.norm3(x) + else: + x = self.norm2(x) + skip = x + x = self.linear2(self.dropout(self.activation(self.linear1(x)))) + x = skip + self.dropout3(x) + if not self.pre_norm: + x = self.norm3(x) + return x + + +def create_sinusoidal_pos_embedding(num_positions: int, dimension: int) -> Tensor: + """1D sinusoidal positional embeddings as in Attention is All You Need. + + Args: + num_positions: Number of token positions required. + Returns: (num_positions, dimension) position embeddings (the first dimension is the batch dimension). + + """ + + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / dimension) for hid_j in range(dimension)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(num_positions)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + return torch.from_numpy(sinusoid_table).float() + + +class ACTSinusoidalPositionEmbedding2d(nn.Module): + """2D sinusoidal positional embeddings similar to what's presented in Attention Is All You Need. + + The variation is that the position indices are normalized in [0, 2π] (not quite: the lower bound is 1/H + for the vertical direction, and 1/W for the horizontal direction. + """ + + def __init__(self, dimension: int): + """ + Args: + dimension: The desired dimension of the embeddings. + """ + super().__init__() + self.dimension = dimension + self._two_pi = 2 * math.pi + self._eps = 1e-6 + # Inverse "common ratio" for the geometric progression in sinusoid frequencies. + self._temperature = 10000 + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: A (B, C, H, W) batch of 2D feature map to generate the embeddings for. + Returns: + A (1, C, H, W) batch of corresponding sinusoidal positional embeddings. + """ + not_mask = torch.ones_like(x[0, :1]) # (1, H, W) + # Note: These are like range(1, H+1) and range(1, W+1) respectively, but in most implementations + # they would be range(0, H) and range(0, W). Keeping it at as is to match the original code. + y_range = not_mask.cumsum(1, dtype=torch.float32) + x_range = not_mask.cumsum(2, dtype=torch.float32) + + # "Normalize" the position index such that it ranges in [0, 2π]. + # Note: Adding epsilon on the denominator should not be needed as all values of y_embed and x_range + # are non-zero by construction. This is an artifact of the original code. + y_range = y_range / (y_range[:, -1:, :] + self._eps) * self._two_pi + x_range = x_range / (x_range[:, :, -1:] + self._eps) * self._two_pi + + inverse_frequency = self._temperature ** ( + 2 * (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2) / self.dimension + ) + + x_range = x_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1) + y_range = y_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1) + + # Note: this stack then flatten operation results in interleaved sine and cosine terms. + # pos_embed_x and pos_embed_y are (1, H, W, C // 2). + pos_embed_x = torch.stack((x_range[..., 0::2].sin(), x_range[..., 1::2].cos()), dim=-1).flatten(3) + pos_embed_y = torch.stack((y_range[..., 0::2].sin(), y_range[..., 1::2].cos()), dim=-1).flatten(3) + pos_embed = torch.cat((pos_embed_y, pos_embed_x), dim=3).permute(0, 3, 1, 2) # (1, C, H, W) + + return pos_embed + + +def get_activation_fn(activation: str) -> Callable: + """Return an activation function given a string.""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu/glu, not {activation}.") diff --git a/lerobot/common/policies/diffusion/configuration_diffusion.py b/lerobot/common/policies/diffusion/configuration_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..d62bd195c160afece377fef8b74934f7d8264008 --- /dev/null +++ b/lerobot/common/policies/diffusion/configuration_diffusion.py @@ -0,0 +1,237 @@ +#!/usr/bin/env python + +# Copyright 2024 Columbia Artificial Intelligence, Robotics Lab, +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamConfig +from lerobot.common.optim.schedulers import DiffuserSchedulerConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import NormalizationMode + + +@PreTrainedConfig.register_subclass("diffusion") +@dataclass +class DiffusionConfig(PreTrainedConfig): + """Configuration class for DiffusionPolicy. + + Defaults are configured for training with PushT providing proprioceptive and single camera observations. + + The parameters you will most likely need to change are the ones which depend on the environment / sensors. + Those are: `input_shapes` and `output_shapes`. + + Notes on the inputs and outputs: + - "observation.state" is required as an input key. + - Either: + - At least one key starting with "observation.image is required as an input. + AND/OR + - The key "observation.environment_state" is required as input. + - If there are multiple keys beginning with "observation.image" they are treated as multiple camera + views. Right now we only support all images having the same shape. + - "action" is required as an output key. + + Args: + n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the + current step and additional steps going back). + horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`. + n_action_steps: The number of action steps to run in the environment for one invocation of the policy. + See `DiffusionPolicy.select_action` for more details. + input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents + the input data name, and the value is a list indicating the dimensions of the corresponding data. + For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], + indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't + include batch dimension or temporal dimension. + output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents + the output data name, and the value is a list indicating the dimensions of the corresponding data. + For example, "action" refers to an output shape of [14], indicating 14-dimensional actions. + Importantly, `output_shapes` doesn't include batch dimension or temporal dimension. + input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), + and the value specifies the normalization mode to apply. The two available modes are "mean_std" + which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a + [-1, 1] range. + output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the + original scale. Note that this is also used for normalizing the training targets. + vision_backbone: Name of the torchvision resnet backbone to use for encoding images. + crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit + within the image size. If None, no cropping is done. + crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval + mode). + pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone. + `None` means no pretrained weights. + use_group_norm: Whether to replace batch normalization with group normalization in the backbone. + The group sizes are set to be about 16 (to be precise, feature_dim // 16). + spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax. + use_separate_rgb_encoders_per_camera: Whether to use a separate RGB encoder for each camera view. + down_dims: Feature dimension for each stage of temporal downsampling in the diffusion modeling Unet. + You may provide a variable number of dimensions, therefore also controlling the degree of + downsampling. + kernel_size: The convolutional kernel size of the diffusion modeling Unet. + n_groups: Number of groups used in the group norm of the Unet's convolutional blocks. + diffusion_step_embed_dim: The Unet is conditioned on the diffusion timestep via a small non-linear + network. This is the output dimension of that network, i.e., the embedding dimension. + use_film_scale_modulation: FiLM (https://huggingface.co/papers/1709.07871) is used for the Unet conditioning. + Bias modulation is used be default, while this parameter indicates whether to also use scale + modulation. + noise_scheduler_type: Name of the noise scheduler to use. Supported options: ["DDPM", "DDIM"]. + num_train_timesteps: Number of diffusion steps for the forward diffusion schedule. + beta_schedule: Name of the diffusion beta schedule as per DDPMScheduler from Hugging Face diffusers. + beta_start: Beta value for the first forward-diffusion step. + beta_end: Beta value for the last forward-diffusion step. + prediction_type: The type of prediction that the diffusion modeling Unet makes. Choose from "epsilon" + or "sample". These have equivalent outcomes from a latent variable modeling perspective, but + "epsilon" has been shown to work better in many deep neural network settings. + clip_sample: Whether to clip the sample to [-`clip_sample_range`, +`clip_sample_range`] for each + denoising step at inference time. WARNING: you will need to make sure your action-space is + normalized to fit within this range. + clip_sample_range: The magnitude of the clipping range as described above. + num_inference_steps: Number of reverse diffusion steps to use at inference time (steps are evenly + spaced). If not provided, this defaults to be the same as `num_train_timesteps`. + do_mask_loss_for_padding: Whether to mask the loss when there are copy-padded actions. See + `LeRobotDataset` and `load_previous_and_future_frames` for more information. Note, this defaults + to False as the original Diffusion Policy implementation does the same. + """ + + # Inputs / output structure. + n_obs_steps: int = 2 + horizon: int = 16 + n_action_steps: int = 8 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.MEAN_STD, + "STATE": NormalizationMode.MIN_MAX, + "ACTION": NormalizationMode.MIN_MAX, + } + ) + + # The original implementation doesn't sample frames for the last 7 steps, + # which avoids excessive padding and leads to improved training results. + drop_n_last_frames: int = 7 # horizon - n_action_steps - n_obs_steps + 1 + + # Architecture / modeling. + # Vision backbone. + vision_backbone: str = "resnet18" + crop_shape: tuple[int, int] | None = (84, 84) + crop_is_random: bool = True + pretrained_backbone_weights: str | None = None + use_group_norm: bool = True + spatial_softmax_num_keypoints: int = 32 + use_separate_rgb_encoder_per_camera: bool = False + # Unet. + down_dims: tuple[int, ...] = (512, 1024, 2048) + kernel_size: int = 5 + n_groups: int = 8 + diffusion_step_embed_dim: int = 128 + use_film_scale_modulation: bool = True + # Noise scheduler. + noise_scheduler_type: str = "DDPM" + num_train_timesteps: int = 100 + beta_schedule: str = "squaredcos_cap_v2" + beta_start: float = 0.0001 + beta_end: float = 0.02 + prediction_type: str = "epsilon" + clip_sample: bool = True + clip_sample_range: float = 1.0 + + # Inference + num_inference_steps: int | None = None + + # Loss computation + do_mask_loss_for_padding: bool = False + + # Training presets + optimizer_lr: float = 1e-4 + optimizer_betas: tuple = (0.95, 0.999) + optimizer_eps: float = 1e-8 + optimizer_weight_decay: float = 1e-6 + scheduler_name: str = "cosine" + scheduler_warmup_steps: int = 500 + + def __post_init__(self): + super().__post_init__() + + """Input validation (not exhaustive).""" + if not self.vision_backbone.startswith("resnet"): + raise ValueError( + f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}." + ) + + supported_prediction_types = ["epsilon", "sample"] + if self.prediction_type not in supported_prediction_types: + raise ValueError( + f"`prediction_type` must be one of {supported_prediction_types}. Got {self.prediction_type}." + ) + supported_noise_schedulers = ["DDPM", "DDIM"] + if self.noise_scheduler_type not in supported_noise_schedulers: + raise ValueError( + f"`noise_scheduler_type` must be one of {supported_noise_schedulers}. " + f"Got {self.noise_scheduler_type}." + ) + + # Check that the horizon size and U-Net downsampling is compatible. + # U-Net downsamples by 2 with each stage. + downsampling_factor = 2 ** len(self.down_dims) + if self.horizon % downsampling_factor != 0: + raise ValueError( + "The horizon should be an integer multiple of the downsampling factor (which is determined " + f"by `len(down_dims)`). Got {self.horizon=} and {self.down_dims=}" + ) + + def get_optimizer_preset(self) -> AdamConfig: + return AdamConfig( + lr=self.optimizer_lr, + betas=self.optimizer_betas, + eps=self.optimizer_eps, + weight_decay=self.optimizer_weight_decay, + ) + + def get_scheduler_preset(self) -> DiffuserSchedulerConfig: + return DiffuserSchedulerConfig( + name=self.scheduler_name, + num_warmup_steps=self.scheduler_warmup_steps, + ) + + def validate_features(self) -> None: + if len(self.image_features) == 0 and self.env_state_feature is None: + raise ValueError("You must provide at least one image or the environment state among the inputs.") + + if self.crop_shape is not None: + for key, image_ft in self.image_features.items(): + if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]: + raise ValueError( + f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} " + f"for `crop_shape` and {image_ft.shape} for " + f"`{key}`." + ) + + # Check that all input images have the same shape. + first_image_key, first_image_ft = next(iter(self.image_features.items())) + for key, image_ft in self.image_features.items(): + if image_ft.shape != first_image_ft.shape: + raise ValueError( + f"`{key}` does not match `{first_image_key}`, but we expect all image shapes to match." + ) + + @property + def observation_delta_indices(self) -> list: + return list(range(1 - self.n_obs_steps, 1)) + + @property + def action_delta_indices(self) -> list: + return list(range(1 - self.n_obs_steps, 1 - self.n_obs_steps + self.horizon)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/diffusion/modeling_diffusion.py b/lerobot/common/policies/diffusion/modeling_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..7335804bfb26269e0f95806122b05cd3044ac2b6 --- /dev/null +++ b/lerobot/common/policies/diffusion/modeling_diffusion.py @@ -0,0 +1,765 @@ +#!/usr/bin/env python + +# Copyright 2024 Columbia Artificial Intelligence, Robotics Lab, +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion" + +TODO(alexander-soare): + - Remove reliance on diffusers for DDPMScheduler and LR scheduler. +""" + +import math +from collections import deque +from typing import Callable + +import einops +import numpy as np +import torch +import torch.nn.functional as F # noqa: N812 +import torchvision +from diffusers.schedulers.scheduling_ddim import DDIMScheduler +from diffusers.schedulers.scheduling_ddpm import DDPMScheduler +from torch import Tensor, nn + +from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE +from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.utils import ( + get_device_from_parameters, + get_dtype_from_parameters, + get_output_shape, + populate_queues, +) + + +class DiffusionPolicy(PreTrainedPolicy): + """ + Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion" + (paper: https://huggingface.co/papers/2303.04137, code: https://github.com/real-stanford/diffusion_policy). + """ + + config_class = DiffusionConfig + name = "diffusion" + + def __init__( + self, + config: DiffusionConfig, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + super().__init__(config) + config.validate_features() + self.config = config + + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + # queues are populated during rollout of the policy, they contain the n latest observations and actions + self._queues = None + + self.diffusion = DiffusionModel(config) + + self.reset() + + def get_optim_params(self) -> dict: + return self.diffusion.parameters() + + def reset(self): + """Clear observation and action queues. Should be called on `env.reset()`""" + self._queues = { + "observation.state": deque(maxlen=self.config.n_obs_steps), + "action": deque(maxlen=self.config.n_action_steps), + } + if self.config.image_features: + self._queues["observation.images"] = deque(maxlen=self.config.n_obs_steps) + if self.config.env_state_feature: + self._queues["observation.environment_state"] = deque(maxlen=self.config.n_obs_steps) + + @torch.no_grad + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations. + + This method handles caching a history of observations and an action trajectory generated by the + underlying diffusion model. Here's how it works: + - `n_obs_steps` steps worth of observations are cached (for the first steps, the observation is + copied `n_obs_steps` times to fill the cache). + - The diffusion model generates `horizon` steps worth of actions. + - `n_action_steps` worth of actions are actually kept for execution, starting from the current step. + Schematically this looks like: + ---------------------------------------------------------------------------------------------- + (legend: o = n_obs_steps, h = horizon, a = n_action_steps) + |timestep | n-o+1 | n-o+2 | ..... | n | ..... | n+a-1 | n+a | ..... | n-o+h | + |observation is used | YES | YES | YES | YES | NO | NO | NO | NO | NO | + |action is generated | YES | YES | YES | YES | YES | YES | YES | YES | YES | + |action is used | NO | NO | NO | YES | YES | YES | NO | NO | NO | + ---------------------------------------------------------------------------------------------- + Note that this means we require: `n_action_steps <= horizon - n_obs_steps + 1`. Also, note that + "horizon" may not the best name to describe what the variable actually means, because this period is + actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past. + """ + batch = self.normalize_inputs(batch) + if self.config.image_features: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.images"] = torch.stack( + [batch[key] for key in self.config.image_features], dim=-4 + ) + # Note: It's important that this happens after stacking the images into a single key. + self._queues = populate_queues(self._queues, batch) + + if len(self._queues["action"]) == 0: + # stack n latest observations from the queue + batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues} + actions = self.diffusion.generate_actions(batch) + + # TODO(rcadene): make above methods return output dictionary? + actions = self.unnormalize_outputs({"action": actions})["action"] + + self._queues["action"].extend(actions.transpose(0, 1)) + + action = self._queues["action"].popleft() + return action + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, None]: + """Run the batch through the model and compute the loss for training or validation.""" + batch = self.normalize_inputs(batch) + if self.config.image_features: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.images"] = torch.stack( + [batch[key] for key in self.config.image_features], dim=-4 + ) + batch = self.normalize_targets(batch) + loss = self.diffusion.compute_loss(batch) + # no output_dict so returning None + return loss, None + + +def _make_noise_scheduler(name: str, **kwargs: dict) -> DDPMScheduler | DDIMScheduler: + """ + Factory for noise scheduler instances of the requested type. All kwargs are passed + to the scheduler. + """ + if name == "DDPM": + return DDPMScheduler(**kwargs) + elif name == "DDIM": + return DDIMScheduler(**kwargs) + else: + raise ValueError(f"Unsupported noise scheduler type {name}") + + +class DiffusionModel(nn.Module): + def __init__(self, config: DiffusionConfig): + super().__init__() + self.config = config + + # Build observation encoders (depending on which observations are provided). + global_cond_dim = self.config.robot_state_feature.shape[0] + if self.config.image_features: + num_images = len(self.config.image_features) + if self.config.use_separate_rgb_encoder_per_camera: + encoders = [DiffusionRgbEncoder(config) for _ in range(num_images)] + self.rgb_encoder = nn.ModuleList(encoders) + global_cond_dim += encoders[0].feature_dim * num_images + else: + self.rgb_encoder = DiffusionRgbEncoder(config) + global_cond_dim += self.rgb_encoder.feature_dim * num_images + if self.config.env_state_feature: + global_cond_dim += self.config.env_state_feature.shape[0] + + self.unet = DiffusionConditionalUnet1d(config, global_cond_dim=global_cond_dim * config.n_obs_steps) + + self.noise_scheduler = _make_noise_scheduler( + config.noise_scheduler_type, + num_train_timesteps=config.num_train_timesteps, + beta_start=config.beta_start, + beta_end=config.beta_end, + beta_schedule=config.beta_schedule, + clip_sample=config.clip_sample, + clip_sample_range=config.clip_sample_range, + prediction_type=config.prediction_type, + ) + + if config.num_inference_steps is None: + self.num_inference_steps = self.noise_scheduler.config.num_train_timesteps + else: + self.num_inference_steps = config.num_inference_steps + + # ========= inference ============ + def conditional_sample( + self, batch_size: int, global_cond: Tensor | None = None, generator: torch.Generator | None = None + ) -> Tensor: + device = get_device_from_parameters(self) + dtype = get_dtype_from_parameters(self) + + # Sample prior. + sample = torch.randn( + size=(batch_size, self.config.horizon, self.config.action_feature.shape[0]), + dtype=dtype, + device=device, + generator=generator, + ) + + self.noise_scheduler.set_timesteps(self.num_inference_steps) + + for t in self.noise_scheduler.timesteps: + # Predict model output. + model_output = self.unet( + sample, + torch.full(sample.shape[:1], t, dtype=torch.long, device=sample.device), + global_cond=global_cond, + ) + # Compute previous image: x_t -> x_t-1 + sample = self.noise_scheduler.step(model_output, t, sample, generator=generator).prev_sample + + return sample + + def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor: + """Encode image features and concatenate them all together along with the state vector.""" + batch_size, n_obs_steps = batch[OBS_STATE].shape[:2] + global_cond_feats = [batch[OBS_STATE]] + # Extract image features. + if self.config.image_features: + if self.config.use_separate_rgb_encoder_per_camera: + # Combine batch and sequence dims while rearranging to make the camera index dimension first. + images_per_camera = einops.rearrange(batch["observation.images"], "b s n ... -> n (b s) ...") + img_features_list = torch.cat( + [ + encoder(images) + for encoder, images in zip(self.rgb_encoder, images_per_camera, strict=True) + ] + ) + # Separate batch and sequence dims back out. The camera index dim gets absorbed into the + # feature dim (effectively concatenating the camera features). + img_features = einops.rearrange( + img_features_list, "(n b s) ... -> b s (n ...)", b=batch_size, s=n_obs_steps + ) + else: + # Combine batch, sequence, and "which camera" dims before passing to shared encoder. + img_features = self.rgb_encoder( + einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...") + ) + # Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the + # feature dim (effectively concatenating the camera features). + img_features = einops.rearrange( + img_features, "(b s n) ... -> b s (n ...)", b=batch_size, s=n_obs_steps + ) + global_cond_feats.append(img_features) + + if self.config.env_state_feature: + global_cond_feats.append(batch[OBS_ENV_STATE]) + + # Concatenate features then flatten to (B, global_cond_dim). + return torch.cat(global_cond_feats, dim=-1).flatten(start_dim=1) + + def generate_actions(self, batch: dict[str, Tensor]) -> Tensor: + """ + This function expects `batch` to have: + { + "observation.state": (B, n_obs_steps, state_dim) + + "observation.images": (B, n_obs_steps, num_cameras, C, H, W) + AND/OR + "observation.environment_state": (B, environment_dim) + } + """ + batch_size, n_obs_steps = batch["observation.state"].shape[:2] + assert n_obs_steps == self.config.n_obs_steps + + # Encode image features and concatenate them all together along with the state vector. + global_cond = self._prepare_global_conditioning(batch) # (B, global_cond_dim) + + # run sampling + actions = self.conditional_sample(batch_size, global_cond=global_cond) + + # Extract `n_action_steps` steps worth of actions (from the current observation). + start = n_obs_steps - 1 + end = start + self.config.n_action_steps + actions = actions[:, start:end] + + return actions + + def compute_loss(self, batch: dict[str, Tensor]) -> Tensor: + """ + This function expects `batch` to have (at least): + { + "observation.state": (B, n_obs_steps, state_dim) + + "observation.images": (B, n_obs_steps, num_cameras, C, H, W) + AND/OR + "observation.environment_state": (B, environment_dim) + + "action": (B, horizon, action_dim) + "action_is_pad": (B, horizon) + } + """ + # Input validation. + assert set(batch).issuperset({"observation.state", "action", "action_is_pad"}) + assert "observation.images" in batch or "observation.environment_state" in batch + n_obs_steps = batch["observation.state"].shape[1] + horizon = batch["action"].shape[1] + assert horizon == self.config.horizon + assert n_obs_steps == self.config.n_obs_steps + + # Encode image features and concatenate them all together along with the state vector. + global_cond = self._prepare_global_conditioning(batch) # (B, global_cond_dim) + + # Forward diffusion. + trajectory = batch["action"] + # Sample noise to add to the trajectory. + eps = torch.randn(trajectory.shape, device=trajectory.device) + # Sample a random noising timestep for each item in the batch. + timesteps = torch.randint( + low=0, + high=self.noise_scheduler.config.num_train_timesteps, + size=(trajectory.shape[0],), + device=trajectory.device, + ).long() + # Add noise to the clean trajectories according to the noise magnitude at each timestep. + noisy_trajectory = self.noise_scheduler.add_noise(trajectory, eps, timesteps) + + # Run the denoising network (that might denoise the trajectory, or attempt to predict the noise). + pred = self.unet(noisy_trajectory, timesteps, global_cond=global_cond) + + # Compute the loss. + # The target is either the original trajectory, or the noise. + if self.config.prediction_type == "epsilon": + target = eps + elif self.config.prediction_type == "sample": + target = batch["action"] + else: + raise ValueError(f"Unsupported prediction type {self.config.prediction_type}") + + loss = F.mse_loss(pred, target, reduction="none") + + # Mask loss wherever the action is padded with copies (edges of the dataset trajectory). + if self.config.do_mask_loss_for_padding: + if "action_is_pad" not in batch: + raise ValueError( + "You need to provide 'action_is_pad' in the batch when " + f"{self.config.do_mask_loss_for_padding=}." + ) + in_episode_bound = ~batch["action_is_pad"] + loss = loss * in_episode_bound.unsqueeze(-1) + + return loss.mean() + + +class SpatialSoftmax(nn.Module): + """ + Spatial Soft Argmax operation described in "Deep Spatial Autoencoders for Visuomotor Learning" by Finn et al. + (https://huggingface.co/papers/1509.06113). A minimal port of the robomimic implementation. + + At a high level, this takes 2D feature maps (from a convnet/ViT) and returns the "center of mass" + of activations of each channel, i.e., keypoints in the image space for the policy to focus on. + + Example: take feature maps of size (512x10x12). We generate a grid of normalized coordinates (10x12x2): + ----------------------------------------------------- + | (-1., -1.) | (-0.82, -1.) | ... | (1., -1.) | + | (-1., -0.78) | (-0.82, -0.78) | ... | (1., -0.78) | + | ... | ... | ... | ... | + | (-1., 1.) | (-0.82, 1.) | ... | (1., 1.) | + ----------------------------------------------------- + This is achieved by applying channel-wise softmax over the activations (512x120) and computing the dot + product with the coordinates (120x2) to get expected points of maximal activation (512x2). + + The example above results in 512 keypoints (corresponding to the 512 input channels). We can optionally + provide num_kp != None to control the number of keypoints. This is achieved by a first applying a learnable + linear mapping (in_channels, H, W) -> (num_kp, H, W). + """ + + def __init__(self, input_shape, num_kp=None): + """ + Args: + input_shape (list): (C, H, W) input feature map shape. + num_kp (int): number of keypoints in output. If None, output will have the same number of channels as input. + """ + super().__init__() + + assert len(input_shape) == 3 + self._in_c, self._in_h, self._in_w = input_shape + + if num_kp is not None: + self.nets = torch.nn.Conv2d(self._in_c, num_kp, kernel_size=1) + self._out_c = num_kp + else: + self.nets = None + self._out_c = self._in_c + + # we could use torch.linspace directly but that seems to behave slightly differently than numpy + # and causes a small degradation in pc_success of pre-trained models. + pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h)) + pos_x = torch.from_numpy(pos_x.reshape(self._in_h * self._in_w, 1)).float() + pos_y = torch.from_numpy(pos_y.reshape(self._in_h * self._in_w, 1)).float() + # register as buffer so it's moved to the correct device. + self.register_buffer("pos_grid", torch.cat([pos_x, pos_y], dim=1)) + + def forward(self, features: Tensor) -> Tensor: + """ + Args: + features: (B, C, H, W) input feature maps. + Returns: + (B, K, 2) image-space coordinates of keypoints. + """ + if self.nets is not None: + features = self.nets(features) + + # [B, K, H, W] -> [B * K, H * W] where K is number of keypoints + features = features.reshape(-1, self._in_h * self._in_w) + # 2d softmax normalization + attention = F.softmax(features, dim=-1) + # [B * K, H * W] x [H * W, 2] -> [B * K, 2] for spatial coordinate mean in x and y dimensions + expected_xy = attention @ self.pos_grid + # reshape to [B, K, 2] + feature_keypoints = expected_xy.view(-1, self._out_c, 2) + + return feature_keypoints + + +class DiffusionRgbEncoder(nn.Module): + """Encodes an RGB image into a 1D feature vector. + + Includes the ability to normalize and crop the image first. + """ + + def __init__(self, config: DiffusionConfig): + super().__init__() + # Set up optional preprocessing. + if config.crop_shape is not None: + self.do_crop = True + # Always use center crop for eval + self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape) + if config.crop_is_random: + self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape) + else: + self.maybe_random_crop = self.center_crop + else: + self.do_crop = False + + # Set up backbone. + backbone_model = getattr(torchvision.models, config.vision_backbone)( + weights=config.pretrained_backbone_weights + ) + # Note: This assumes that the layer4 feature map is children()[-3] + # TODO(alexander-soare): Use a safer alternative. + self.backbone = nn.Sequential(*(list(backbone_model.children())[:-2])) + if config.use_group_norm: + if config.pretrained_backbone_weights: + raise ValueError( + "You can't replace BatchNorm in a pretrained model without ruining the weights!" + ) + self.backbone = _replace_submodules( + root_module=self.backbone, + predicate=lambda x: isinstance(x, nn.BatchNorm2d), + func=lambda x: nn.GroupNorm(num_groups=x.num_features // 16, num_channels=x.num_features), + ) + + # Set up pooling and final layers. + # Use a dry run to get the feature map shape. + # The dummy input should take the number of image channels from `config.image_features` and it should + # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the + # height and width from `config.image_features`. + + # Note: we have a check in the config class to make sure all images have the same shape. + images_shape = next(iter(config.image_features.values())).shape + dummy_shape_h_w = config.crop_shape if config.crop_shape is not None else images_shape[1:] + dummy_shape = (1, images_shape[0], *dummy_shape_h_w) + feature_map_shape = get_output_shape(self.backbone, dummy_shape)[1:] + + self.pool = SpatialSoftmax(feature_map_shape, num_kp=config.spatial_softmax_num_keypoints) + self.feature_dim = config.spatial_softmax_num_keypoints * 2 + self.out = nn.Linear(config.spatial_softmax_num_keypoints * 2, self.feature_dim) + self.relu = nn.ReLU() + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: (B, C, H, W) image tensor with pixel values in [0, 1]. + Returns: + (B, D) image feature. + """ + # Preprocess: maybe crop (if it was set up in the __init__). + if self.do_crop: + if self.training: # noqa: SIM108 + x = self.maybe_random_crop(x) + else: + # Always use center crop for eval. + x = self.center_crop(x) + # Extract backbone feature. + x = torch.flatten(self.pool(self.backbone(x)), start_dim=1) + # Final linear layer with non-linearity. + x = self.relu(self.out(x)) + return x + + +def _replace_submodules( + root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module] +) -> nn.Module: + """ + Args: + root_module: The module for which the submodules need to be replaced + predicate: Takes a module as an argument and must return True if the that module is to be replaced. + func: Takes a module as an argument and returns a new module to replace it with. + Returns: + The root module with its submodules replaced. + """ + if predicate(root_module): + return func(root_module) + + replace_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)] + for *parents, k in replace_list: + parent_module = root_module + if len(parents) > 0: + parent_module = root_module.get_submodule(".".join(parents)) + if isinstance(parent_module, nn.Sequential): + src_module = parent_module[int(k)] + else: + src_module = getattr(parent_module, k) + tgt_module = func(src_module) + if isinstance(parent_module, nn.Sequential): + parent_module[int(k)] = tgt_module + else: + setattr(parent_module, k, tgt_module) + # verify that all BN are replaced + assert not any(predicate(m) for _, m in root_module.named_modules(remove_duplicate=True)) + return root_module + + +class DiffusionSinusoidalPosEmb(nn.Module): + """1D sinusoidal positional embeddings as in Attention is All You Need.""" + + def __init__(self, dim: int): + super().__init__() + self.dim = dim + + def forward(self, x: Tensor) -> Tensor: + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x.unsqueeze(-1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class DiffusionConv1dBlock(nn.Module): + """Conv1d --> GroupNorm --> Mish""" + + def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8): + super().__init__() + + self.block = nn.Sequential( + nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2), + nn.GroupNorm(n_groups, out_channels), + nn.Mish(), + ) + + def forward(self, x): + return self.block(x) + + +class DiffusionConditionalUnet1d(nn.Module): + """A 1D convolutional UNet with FiLM modulation for conditioning. + + Note: this removes local conditioning as compared to the original diffusion policy code. + """ + + def __init__(self, config: DiffusionConfig, global_cond_dim: int): + super().__init__() + + self.config = config + + # Encoder for the diffusion timestep. + self.diffusion_step_encoder = nn.Sequential( + DiffusionSinusoidalPosEmb(config.diffusion_step_embed_dim), + nn.Linear(config.diffusion_step_embed_dim, config.diffusion_step_embed_dim * 4), + nn.Mish(), + nn.Linear(config.diffusion_step_embed_dim * 4, config.diffusion_step_embed_dim), + ) + + # The FiLM conditioning dimension. + cond_dim = config.diffusion_step_embed_dim + global_cond_dim + + # In channels / out channels for each downsampling block in the Unet's encoder. For the decoder, we + # just reverse these. + in_out = [(config.action_feature.shape[0], config.down_dims[0])] + list( + zip(config.down_dims[:-1], config.down_dims[1:], strict=True) + ) + + # Unet encoder. + common_res_block_kwargs = { + "cond_dim": cond_dim, + "kernel_size": config.kernel_size, + "n_groups": config.n_groups, + "use_film_scale_modulation": config.use_film_scale_modulation, + } + self.down_modules = nn.ModuleList([]) + for ind, (dim_in, dim_out) in enumerate(in_out): + is_last = ind >= (len(in_out) - 1) + self.down_modules.append( + nn.ModuleList( + [ + DiffusionConditionalResidualBlock1d(dim_in, dim_out, **common_res_block_kwargs), + DiffusionConditionalResidualBlock1d(dim_out, dim_out, **common_res_block_kwargs), + # Downsample as long as it is not the last block. + nn.Conv1d(dim_out, dim_out, 3, 2, 1) if not is_last else nn.Identity(), + ] + ) + ) + + # Processing in the middle of the auto-encoder. + self.mid_modules = nn.ModuleList( + [ + DiffusionConditionalResidualBlock1d( + config.down_dims[-1], config.down_dims[-1], **common_res_block_kwargs + ), + DiffusionConditionalResidualBlock1d( + config.down_dims[-1], config.down_dims[-1], **common_res_block_kwargs + ), + ] + ) + + # Unet decoder. + self.up_modules = nn.ModuleList([]) + for ind, (dim_out, dim_in) in enumerate(reversed(in_out[1:])): + is_last = ind >= (len(in_out) - 1) + self.up_modules.append( + nn.ModuleList( + [ + # dim_in * 2, because it takes the encoder's skip connection as well + DiffusionConditionalResidualBlock1d(dim_in * 2, dim_out, **common_res_block_kwargs), + DiffusionConditionalResidualBlock1d(dim_out, dim_out, **common_res_block_kwargs), + # Upsample as long as it is not the last block. + nn.ConvTranspose1d(dim_out, dim_out, 4, 2, 1) if not is_last else nn.Identity(), + ] + ) + ) + + self.final_conv = nn.Sequential( + DiffusionConv1dBlock(config.down_dims[0], config.down_dims[0], kernel_size=config.kernel_size), + nn.Conv1d(config.down_dims[0], config.action_feature.shape[0], 1), + ) + + def forward(self, x: Tensor, timestep: Tensor | int, global_cond=None) -> Tensor: + """ + Args: + x: (B, T, input_dim) tensor for input to the Unet. + timestep: (B,) tensor of (timestep_we_are_denoising_from - 1). + global_cond: (B, global_cond_dim) + output: (B, T, input_dim) + Returns: + (B, T, input_dim) diffusion model prediction. + """ + # For 1D convolutions we'll need feature dimension first. + x = einops.rearrange(x, "b t d -> b d t") + + timesteps_embed = self.diffusion_step_encoder(timestep) + + # If there is a global conditioning feature, concatenate it to the timestep embedding. + if global_cond is not None: + global_feature = torch.cat([timesteps_embed, global_cond], axis=-1) + else: + global_feature = timesteps_embed + + # Run encoder, keeping track of skip features to pass to the decoder. + encoder_skip_features: list[Tensor] = [] + for resnet, resnet2, downsample in self.down_modules: + x = resnet(x, global_feature) + x = resnet2(x, global_feature) + encoder_skip_features.append(x) + x = downsample(x) + + for mid_module in self.mid_modules: + x = mid_module(x, global_feature) + + # Run decoder, using the skip features from the encoder. + for resnet, resnet2, upsample in self.up_modules: + x = torch.cat((x, encoder_skip_features.pop()), dim=1) + x = resnet(x, global_feature) + x = resnet2(x, global_feature) + x = upsample(x) + + x = self.final_conv(x) + + x = einops.rearrange(x, "b d t -> b t d") + return x + + +class DiffusionConditionalResidualBlock1d(nn.Module): + """ResNet style 1D convolutional block with FiLM modulation for conditioning.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + cond_dim: int, + kernel_size: int = 3, + n_groups: int = 8, + # Set to True to do scale modulation with FiLM as well as bias modulation (defaults to False meaning + # FiLM just modulates bias). + use_film_scale_modulation: bool = False, + ): + super().__init__() + + self.use_film_scale_modulation = use_film_scale_modulation + self.out_channels = out_channels + + self.conv1 = DiffusionConv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups) + + # FiLM modulation (https://huggingface.co/papers/1709.07871) outputs per-channel bias and (maybe) scale. + cond_channels = out_channels * 2 if use_film_scale_modulation else out_channels + self.cond_encoder = nn.Sequential(nn.Mish(), nn.Linear(cond_dim, cond_channels)) + + self.conv2 = DiffusionConv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups) + + # A final convolution for dimension matching the residual (if needed). + self.residual_conv = ( + nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity() + ) + + def forward(self, x: Tensor, cond: Tensor) -> Tensor: + """ + Args: + x: (B, in_channels, T) + cond: (B, cond_dim) + Returns: + (B, out_channels, T) + """ + out = self.conv1(x) + + # Get condition embedding. Unsqueeze for broadcasting to `out`, resulting in (B, out_channels, 1). + cond_embed = self.cond_encoder(cond).unsqueeze(-1) + if self.use_film_scale_modulation: + # Treat the embedding as a list of scales and biases. + scale = cond_embed[:, : self.out_channels] + bias = cond_embed[:, self.out_channels :] + out = scale * out + bias + else: + # Treat the embedding as biases. + out = out + cond_embed + + out = self.conv2(out) + out = out + self.residual_conv(x) + return out diff --git a/lerobot/common/policies/factory.py b/lerobot/common/policies/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd645f734fb9b7ea1bab5f2b69dd6ccc5c24d08 --- /dev/null +++ b/lerobot/common/policies/factory.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +from torch import nn + +from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata +from lerobot.common.datasets.utils import dataset_to_policy_features +from lerobot.common.envs.configs import EnvConfig +from lerobot.common.envs.utils import env_to_policy_features +from lerobot.common.policies.act.configuration_act import ACTConfig +from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig +from lerobot.common.policies.pi0.configuration_pi0 import PI0Config +from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.sac.configuration_sac import SACConfig +from lerobot.common.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig +from lerobot.common.policies.smolvla.configuration_smolvla import SmolVLAConfig +from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig +from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import FeatureType + + +def get_policy_class(name: str) -> PreTrainedPolicy: + """Get the policy's class and config class given a name (matching the policy class' `name` attribute).""" + if name == "tdmpc": + from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy + + return TDMPCPolicy + elif name == "diffusion": + from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy + + return DiffusionPolicy + elif name == "act": + from lerobot.common.policies.act.modeling_act import ACTPolicy + + return ACTPolicy + elif name == "vqbet": + from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTPolicy + + return VQBeTPolicy + elif name == "pi0": + from lerobot.common.policies.pi0.modeling_pi0 import PI0Policy + + return PI0Policy + elif name == "pi0fast": + from lerobot.common.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy + + return PI0FASTPolicy + elif name == "sac": + from lerobot.common.policies.sac.modeling_sac import SACPolicy + + return SACPolicy + elif name == "reward_classifier": + from lerobot.common.policies.sac.reward_model.modeling_classifier import Classifier + + return Classifier + elif name == "smolvla": + from lerobot.common.policies.smolvla.modeling_smolvla import SmolVLAPolicy + + return SmolVLAPolicy + else: + raise NotImplementedError(f"Policy with name {name} is not implemented.") + + +def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig: + if policy_type == "tdmpc": + return TDMPCConfig(**kwargs) + elif policy_type == "diffusion": + return DiffusionConfig(**kwargs) + elif policy_type == "act": + return ACTConfig(**kwargs) + elif policy_type == "vqbet": + return VQBeTConfig(**kwargs) + elif policy_type == "pi0": + return PI0Config(**kwargs) + elif policy_type == "pi0fast": + return PI0FASTConfig(**kwargs) + elif policy_type == "sac": + return SACConfig(**kwargs) + elif policy_type == "smolvla": + return SmolVLAConfig(**kwargs) + elif policy_type == "reward_classifier": + return RewardClassifierConfig(**kwargs) + else: + raise ValueError(f"Policy type '{policy_type}' is not available.") + + +def make_policy( + cfg: PreTrainedConfig, + ds_meta: LeRobotDatasetMetadata | None = None, + env_cfg: EnvConfig | None = None, +) -> PreTrainedPolicy: + """Make an instance of a policy class. + + This function exists because (for now) we need to parse features from either a dataset or an environment + in order to properly dimension and instantiate a policy for that dataset or environment. + + Args: + cfg (PreTrainedConfig): The config of the policy to make. If `pretrained_path` is set, the policy will + be loaded with the weights from that path. + ds_meta (LeRobotDatasetMetadata | None, optional): Dataset metadata to take input/output shapes and + statistics to use for (un)normalization of inputs/outputs in the policy. Defaults to None. + env_cfg (EnvConfig | None, optional): The config of a gym environment to parse features from. Must be + provided if ds_meta is not. Defaults to None. + + Raises: + ValueError: Either ds_meta or env and env_cfg must be provided. + NotImplementedError: if the policy.type is 'vqbet' and the policy device 'mps' (due to an incompatibility) + + Returns: + PreTrainedPolicy: _description_ + """ + if bool(ds_meta) == bool(env_cfg): + raise ValueError("Either one of a dataset metadata or a sim env must be provided.") + + # NOTE: Currently, if you try to run vqbet with mps backend, you'll get this error. + # TODO(aliberts, rcadene): Implement a check_backend_compatibility in policies? + # NotImplementedError: The operator 'aten::unique_dim' is not currently implemented for the MPS device. If + # you want this op to be added in priority during the prototype phase of this feature, please comment on + # https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment + # variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be + # slower than running natively on MPS. + if cfg.type == "vqbet" and cfg.device == "mps": + raise NotImplementedError( + "Current implementation of VQBeT does not support `mps` backend. " + "Please use `cpu` or `cuda` backend." + ) + + policy_cls = get_policy_class(cfg.type) + + kwargs = {} + if ds_meta is not None: + features = dataset_to_policy_features(ds_meta.features) + kwargs["dataset_stats"] = ds_meta.stats + else: + if not cfg.pretrained_path: + logging.warning( + "You are instantiating a policy from scratch and its features are parsed from an environment " + "rather than a dataset. Normalization modules inside the policy will have infinite values " + "by default without stats from a dataset." + ) + features = env_to_policy_features(env_cfg) + + cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION} + cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features} + kwargs["config"] = cfg + + if cfg.pretrained_path: + # Load a pretrained policy and override the config if needed (for example, if there are inference-time + # hyperparameters that we want to vary). + kwargs["pretrained_name_or_path"] = cfg.pretrained_path + policy = policy_cls.from_pretrained(**kwargs) + else: + # Make a fresh policy. + policy = policy_cls(**kwargs) + + policy.to(cfg.device) + assert isinstance(policy, nn.Module) + + # policy = torch.compile(policy, mode="reduce-overhead") + + return policy diff --git a/lerobot/common/policies/normalize.py b/lerobot/common/policies/normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..b0c3069f147a1a57d458ab061d951ec834e4b98f --- /dev/null +++ b/lerobot/common/policies/normalize.py @@ -0,0 +1,420 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import torch +from torch import Tensor, nn + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature + + +def create_stats_buffers( + features: dict[str, PolicyFeature], + norm_map: dict[str, NormalizationMode], + stats: dict[str, dict[str, Tensor]] | None = None, +) -> dict[str, dict[str, nn.ParameterDict]]: + """ + Create buffers per modality (e.g. "observation.image", "action") containing their mean, std, min, max + statistics. + + Args: (see Normalize and Unnormalize) + + Returns: + dict: A dictionary where keys are modalities and values are `nn.ParameterDict` containing + `nn.Parameters` set to `requires_grad=False`, suitable to not be updated during backpropagation. + """ + stats_buffers = {} + + for key, ft in features.items(): + norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY) + if norm_mode is NormalizationMode.IDENTITY: + continue + + assert isinstance(norm_mode, NormalizationMode) + + shape = tuple(ft.shape) + + if ft.type is FeatureType.VISUAL: + # sanity checks + assert len(shape) == 3, f"number of dimensions of {key} != 3 ({shape=}" + c, h, w = shape + assert c < h and c < w, f"{key} is not channel first ({shape=})" + # override image shape to be invariant to height and width + shape = (c, 1, 1) + + # Note: we initialize mean, std, min, max to infinity. They should be overwritten + # downstream by `stats` or `policy.load_state_dict`, as expected. During forward, + # we assert they are not infinity anymore. + + buffer = {} + if norm_mode is NormalizationMode.MEAN_STD: + mean = torch.ones(shape, dtype=torch.float32) * torch.inf + std = torch.ones(shape, dtype=torch.float32) * torch.inf + buffer = nn.ParameterDict( + { + "mean": nn.Parameter(mean, requires_grad=False), + "std": nn.Parameter(std, requires_grad=False), + } + ) + elif norm_mode is NormalizationMode.MIN_MAX: + min = torch.ones(shape, dtype=torch.float32) * torch.inf + max = torch.ones(shape, dtype=torch.float32) * torch.inf + buffer = nn.ParameterDict( + { + "min": nn.Parameter(min, requires_grad=False), + "max": nn.Parameter(max, requires_grad=False), + } + ) + + # TODO(aliberts, rcadene): harmonize this to only use one framework (np or torch) + if stats: + if isinstance(stats[key]["mean"], np.ndarray): + if norm_mode is NormalizationMode.MEAN_STD: + buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32) + buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32) + elif norm_mode is NormalizationMode.MIN_MAX: + buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32) + buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32) + elif isinstance(stats[key]["mean"], torch.Tensor): + # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated + # tensors anywhere (for example, when we use the same stats for normalization and + # unnormalization). See the logic here + # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97. + if norm_mode is NormalizationMode.MEAN_STD: + buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32) + buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32) + elif norm_mode is NormalizationMode.MIN_MAX: + buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32) + buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32) + else: + type_ = type(stats[key]["mean"]) + raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.") + + stats_buffers[key] = buffer + return stats_buffers + + +def _no_stats_error_str(name: str) -> str: + return ( + f"`{name}` is infinity. You should either initialize with `stats` as an argument, or use a " + "pretrained model." + ) + + +class Normalize(nn.Module): + """Normalizes data (e.g. "observation.image") for more stable and faster convergence during training.""" + + def __init__( + self, + features: dict[str, PolicyFeature], + norm_map: dict[str, NormalizationMode], + stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + shapes (dict): A dictionary where keys are input modalities (e.g. "observation.image") and values + are their shapes (e.g. `[3,96,96]`]). These shapes are used to create the tensor buffer containing + mean, std, min, max statistics. If the provided `shapes` contain keys related to images, the shape + is adjusted to be invariant to height and width, assuming a channel-first (c, h, w) format. + modes (dict): A dictionary where keys are output modalities (e.g. "observation.image") and values + are their normalization modes among: + - "mean_std": subtract the mean and divide by standard deviation. + - "min_max": map to [-1, 1] range. + stats (dict, optional): A dictionary where keys are output modalities (e.g. "observation.image") + and values are dictionaries of statistic types and their values (e.g. + `{"mean": torch.randn(3,1,1)}, "std": torch.randn(3,1,1)}`). If provided, as expected for + training the model for the first time, these statistics will overwrite the default buffers. If + not provided, as expected for finetuning or evaluation, the default buffers should to be + overwritten by a call to `policy.load_state_dict(state_dict)`. That way, initializing the + dataset is not needed to get the stats, since they are already in the policy state_dict. + """ + super().__init__() + self.features = features + self.norm_map = norm_map + self.stats = stats + stats_buffers = create_stats_buffers(features, norm_map, stats) + for key, buffer in stats_buffers.items(): + setattr(self, "buffer_" + key.replace(".", "_"), buffer) + + # TODO(rcadene): should we remove torch.no_grad? + @torch.no_grad + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + # TODO: Remove this shallow copy + batch = dict(batch) # shallow copy avoids mutating the input batch + for key, ft in self.features.items(): + if key not in batch: + # FIXME(aliberts, rcadene): This might lead to silent fail! + continue + + norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) + if norm_mode is NormalizationMode.IDENTITY: + continue + + buffer = getattr(self, "buffer_" + key.replace(".", "_")) + + if norm_mode is NormalizationMode.MEAN_STD: + mean = buffer["mean"] + std = buffer["std"] + assert not torch.isinf(mean).any(), _no_stats_error_str("mean") + assert not torch.isinf(std).any(), _no_stats_error_str("std") + batch[key] = (batch[key] - mean) / (std + 1e-8) + elif norm_mode is NormalizationMode.MIN_MAX: + min = buffer["min"] + max = buffer["max"] + assert not torch.isinf(min).any(), _no_stats_error_str("min") + assert not torch.isinf(max).any(), _no_stats_error_str("max") + # normalize to [0,1] + batch[key] = (batch[key] - min) / (max - min + 1e-8) + # normalize to [-1, 1] + batch[key] = batch[key] * 2 - 1 + else: + raise ValueError(norm_mode) + return batch + + +class Unnormalize(nn.Module): + """ + Similar to `Normalize` but unnormalizes output data (e.g. `{"action": torch.randn(b,c)}`) in their + original range used by the environment. + """ + + def __init__( + self, + features: dict[str, PolicyFeature], + norm_map: dict[str, NormalizationMode], + stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + shapes (dict): A dictionary where keys are input modalities (e.g. "observation.image") and values + are their shapes (e.g. `[3,96,96]`]). These shapes are used to create the tensor buffer containing + mean, std, min, max statistics. If the provided `shapes` contain keys related to images, the shape + is adjusted to be invariant to height and width, assuming a channel-first (c, h, w) format. + modes (dict): A dictionary where keys are output modalities (e.g. "observation.image") and values + are their normalization modes among: + - "mean_std": subtract the mean and divide by standard deviation. + - "min_max": map to [-1, 1] range. + stats (dict, optional): A dictionary where keys are output modalities (e.g. "observation.image") + and values are dictionaries of statistic types and their values (e.g. + `{"mean": torch.randn(3,1,1)}, "std": torch.randn(3,1,1)}`). If provided, as expected for + training the model for the first time, these statistics will overwrite the default buffers. If + not provided, as expected for finetuning or evaluation, the default buffers should to be + overwritten by a call to `policy.load_state_dict(state_dict)`. That way, initializing the + dataset is not needed to get the stats, since they are already in the policy state_dict. + """ + super().__init__() + self.features = features + self.norm_map = norm_map + self.stats = stats + # `self.buffer_observation_state["mean"]` contains `torch.tensor(state_dim)` + stats_buffers = create_stats_buffers(features, norm_map, stats) + for key, buffer in stats_buffers.items(): + setattr(self, "buffer_" + key.replace(".", "_"), buffer) + + # TODO(rcadene): should we remove torch.no_grad? + @torch.no_grad + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + batch = dict(batch) # shallow copy avoids mutating the input batch + for key, ft in self.features.items(): + if key not in batch: + continue + + norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) + if norm_mode is NormalizationMode.IDENTITY: + continue + + buffer = getattr(self, "buffer_" + key.replace(".", "_")) + + if norm_mode is NormalizationMode.MEAN_STD: + mean = buffer["mean"] + std = buffer["std"] + assert not torch.isinf(mean).any(), _no_stats_error_str("mean") + assert not torch.isinf(std).any(), _no_stats_error_str("std") + batch[key] = batch[key] * std + mean + elif norm_mode is NormalizationMode.MIN_MAX: + min = buffer["min"] + max = buffer["max"] + assert not torch.isinf(min).any(), _no_stats_error_str("min") + assert not torch.isinf(max).any(), _no_stats_error_str("max") + batch[key] = (batch[key] + 1) / 2 + batch[key] = batch[key] * (max - min) + min + else: + raise ValueError(norm_mode) + return batch + + +# TODO (azouitine): We should replace all normalization on the policies with register_buffer normalization +# and remove the `Normalize` and `Unnormalize` classes. +def _initialize_stats_buffers( + module: nn.Module, + features: dict[str, PolicyFeature], + norm_map: dict[str, NormalizationMode], + stats: dict[str, dict[str, Tensor]] | None = None, +) -> None: + """Register statistics buffers (mean/std or min/max) on the given *module*. + + The logic matches the previous constructors of `NormalizeBuffer` and `UnnormalizeBuffer`, + but is factored out so it can be reused by both classes and stay in sync. + """ + for key, ft in features.items(): + norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY) + if norm_mode is NormalizationMode.IDENTITY: + continue + + shape: tuple[int, ...] = tuple(ft.shape) + if ft.type is FeatureType.VISUAL: + # reduce spatial dimensions, keep channel dimension only + c, *_ = shape + shape = (c, 1, 1) + + prefix = key.replace(".", "_") + + if norm_mode is NormalizationMode.MEAN_STD: + mean = torch.full(shape, torch.inf, dtype=torch.float32) + std = torch.full(shape, torch.inf, dtype=torch.float32) + + if stats and key in stats and "mean" in stats[key] and "std" in stats[key]: + mean_data = stats[key]["mean"] + std_data = stats[key]["std"] + if isinstance(mean_data, torch.Tensor): + # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated + # tensors anywhere (for example, when we use the same stats for normalization and + # unnormalization). See the logic here + # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97. + mean = mean_data.clone().to(dtype=torch.float32) + std = std_data.clone().to(dtype=torch.float32) + else: + raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).") + + module.register_buffer(f"{prefix}_mean", mean) + module.register_buffer(f"{prefix}_std", std) + continue + + if norm_mode is NormalizationMode.MIN_MAX: + min_val = torch.full(shape, torch.inf, dtype=torch.float32) + max_val = torch.full(shape, torch.inf, dtype=torch.float32) + + if stats and key in stats and "min" in stats[key] and "max" in stats[key]: + min_data = stats[key]["min"] + max_data = stats[key]["max"] + if isinstance(min_data, torch.Tensor): + min_val = min_data.clone().to(dtype=torch.float32) + max_val = max_data.clone().to(dtype=torch.float32) + else: + raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).") + + module.register_buffer(f"{prefix}_min", min_val) + module.register_buffer(f"{prefix}_max", max_val) + continue + + raise ValueError(norm_mode) + + +class NormalizeBuffer(nn.Module): + """Same as `Normalize` but statistics are stored as registered buffers rather than parameters.""" + + def __init__( + self, + features: dict[str, PolicyFeature], + norm_map: dict[str, NormalizationMode], + stats: dict[str, dict[str, Tensor]] | None = None, + ): + super().__init__() + self.features = features + self.norm_map = norm_map + + _initialize_stats_buffers(self, features, norm_map, stats) + + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + batch = dict(batch) + for key, ft in self.features.items(): + if key not in batch: + continue + + norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) + if norm_mode is NormalizationMode.IDENTITY: + continue + + prefix = key.replace(".", "_") + + if norm_mode is NormalizationMode.MEAN_STD: + mean = getattr(self, f"{prefix}_mean") + std = getattr(self, f"{prefix}_std") + assert not torch.isinf(mean).any(), _no_stats_error_str("mean") + assert not torch.isinf(std).any(), _no_stats_error_str("std") + batch[key] = (batch[key] - mean) / (std + 1e-8) + continue + + if norm_mode is NormalizationMode.MIN_MAX: + min_val = getattr(self, f"{prefix}_min") + max_val = getattr(self, f"{prefix}_max") + assert not torch.isinf(min_val).any(), _no_stats_error_str("min") + assert not torch.isinf(max_val).any(), _no_stats_error_str("max") + batch[key] = (batch[key] - min_val) / (max_val - min_val + 1e-8) + batch[key] = batch[key] * 2 - 1 + continue + + raise ValueError(norm_mode) + + return batch + + +class UnnormalizeBuffer(nn.Module): + """Inverse operation of `NormalizeBuffer`. Uses registered buffers for statistics.""" + + def __init__( + self, + features: dict[str, PolicyFeature], + norm_map: dict[str, NormalizationMode], + stats: dict[str, dict[str, Tensor]] | None = None, + ): + super().__init__() + self.features = features + self.norm_map = norm_map + + _initialize_stats_buffers(self, features, norm_map, stats) + + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + # batch = dict(batch) + for key, ft in self.features.items(): + if key not in batch: + continue + + norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) + if norm_mode is NormalizationMode.IDENTITY: + continue + + prefix = key.replace(".", "_") + + if norm_mode is NormalizationMode.MEAN_STD: + mean = getattr(self, f"{prefix}_mean") + std = getattr(self, f"{prefix}_std") + assert not torch.isinf(mean).any(), _no_stats_error_str("mean") + assert not torch.isinf(std).any(), _no_stats_error_str("std") + batch[key] = batch[key] * std + mean + continue + + if norm_mode is NormalizationMode.MIN_MAX: + min_val = getattr(self, f"{prefix}_min") + max_val = getattr(self, f"{prefix}_max") + assert not torch.isinf(min_val).any(), _no_stats_error_str("min") + assert not torch.isinf(max_val).any(), _no_stats_error_str("max") + batch[key] = (batch[key] + 1) / 2 + batch[key] = batch[key] * (max_val - min_val) + min_val + continue + + raise ValueError(norm_mode) + + return batch diff --git a/lerobot/common/policies/pi0/configuration_pi0.py b/lerobot/common/policies/pi0/configuration_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..2732b620a01bf0dc3c3ebe927c650f635c7b89e6 --- /dev/null +++ b/lerobot/common/policies/pi0/configuration_pi0.py @@ -0,0 +1,149 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamWConfig +from lerobot.common.optim.schedulers import ( + CosineDecayWithWarmupSchedulerConfig, +) +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature + + +@PreTrainedConfig.register_subclass("pi0") +@dataclass +class PI0Config(PreTrainedConfig): + # Input / output structure. + n_obs_steps: int = 1 + chunk_size: int = 50 + n_action_steps: int = 50 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.IDENTITY, + "STATE": NormalizationMode.MEAN_STD, + "ACTION": NormalizationMode.MEAN_STD, + } + ) + + # Shorter state and action vectors will be padded + max_state_dim: int = 32 + max_action_dim: int = 32 + + # Image preprocessing + resize_imgs_with_padding: tuple[int, int] = (224, 224) + + # Add empty images. Used by pi0_aloha_sim which adds the empty + # left and right wrist cameras in addition to the top camera. + empty_cameras: int = 0 + + # Converts the joint and gripper values from the standard Aloha space to + # the space used by the pi internal runtime which was used to train the base model. + adapt_to_pi_aloha: bool = False + + # Converts joint dimensions to deltas with respect to the current state before passing to the model. + # Gripper dimensions will remain in absolute values. + use_delta_joint_actions_aloha: bool = False + + # Tokenizer + tokenizer_max_length: int = 48 + + # Projector + proj_width: int = 1024 + + # Decoding + num_steps: int = 10 + + # Attention utils + use_cache: bool = True + attention_implementation: str = "eager" # or fa2, flex + + # Finetuning settings + freeze_vision_encoder: bool = True + train_expert_only: bool = False + train_state_proj: bool = True + + # Training presets + optimizer_lr: float = 2.5e-5 + optimizer_betas: tuple[float, float] = (0.9, 0.95) + optimizer_eps: float = 1e-8 + optimizer_weight_decay: float = 1e-10 + + scheduler_warmup_steps: int = 1_000 + scheduler_decay_steps: int = 30_000 + scheduler_decay_lr: float = 2.5e-6 + + # TODO: Add EMA + + def __post_init__(self): + super().__post_init__() + + # TODO(Steven): Validate device and amp? in all policy configs? + """Input validation (not exhaustive).""" + if self.n_action_steps > self.chunk_size: + raise ValueError( + f"The chunk size is the upper bound for the number of action steps per model invocation. Got " + f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`." + ) + if self.n_obs_steps != 1: + raise ValueError( + f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`" + ) + + if self.use_delta_joint_actions_aloha: + raise NotImplementedError( + "`use_delta_joint_actions_aloha` is used by pi0 for aloha real models. It is not ported yet in LeRobot." + ) + + def validate_features(self) -> None: + # TODO: implement value error + # if not self.image_features and not self.env_state_feature: + # raise ValueError("You must provide at least one image or the environment state among the inputs.") + + for i in range(self.empty_cameras): + key = f"observation.images.empty_camera_{i}" + empty_camera = PolicyFeature( + type=FeatureType.VISUAL, + shape=(3, 480, 640), + ) + self.input_features[key] = empty_camera + + def get_optimizer_preset(self) -> AdamWConfig: + return AdamWConfig( + lr=self.optimizer_lr, + betas=self.optimizer_betas, + eps=self.optimizer_eps, + weight_decay=self.optimizer_weight_decay, + ) + + def get_scheduler_preset(self): + return CosineDecayWithWarmupSchedulerConfig( + peak_lr=self.optimizer_lr, + decay_lr=self.scheduler_decay_lr, + num_warmup_steps=self.scheduler_warmup_steps, + num_decay_steps=self.scheduler_decay_steps, + ) + + @property + def observation_delta_indices(self) -> None: + return None + + @property + def action_delta_indices(self) -> list: + return list(range(self.chunk_size)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/pi0/conversion_scripts/benchmark.py b/lerobot/common/policies/pi0/conversion_scripts/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..2b2cd232e74e8aefc72a6fac202939e31ce47c9a --- /dev/null +++ b/lerobot/common/policies/pi0/conversion_scripts/benchmark.py @@ -0,0 +1,82 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.policies.factory import make_policy +from lerobot.configs.policies import PreTrainedConfig + +torch.backends.cudnn.benchmark = True + + +def main(): + device = "cuda" + dataset_repo_id = "danaaubakirova/koch_test" + # model_name = "pi0_base" + # ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch" + ckpt_torch_dir = "lerobot/pi0" + + dataset = LeRobotDataset(dataset_repo_id, episodes=[0]) + + dataloader = torch.utils.data.DataLoader( + dataset, + num_workers=0, + batch_size=1, + ) + + batch = next(iter(dataloader)) + + # To device + for k in batch: + if isinstance(batch[k], torch.Tensor): + batch[k] = batch[k].to(device=device, dtype=torch.float32) + + cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir) + cfg.pretrained_path = ckpt_torch_dir + policy = make_policy(cfg, ds_meta=dataset.meta) + + # policy = torch.compile(policy, mode="reduce-overhead") + + warmup_iters = 10 + benchmark_iters = 30 + + # Warmup + for _ in range(warmup_iters): + torch.cuda.synchronize() + policy.select_action(batch) + policy.reset() + torch.cuda.synchronize() + + # Benchmark + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + + start_event.record() + for _ in range(benchmark_iters): + policy.select_action(batch) + policy.reset() + end_event.record() + + # Synchronize and measure time + torch.cuda.synchronize() + elapsed_time_ms = start_event.elapsed_time(end_event) + + avg_time_per_iter = elapsed_time_ms / benchmark_iters + print(f"Average execution time per iteration: {avg_time_per_iter:.3f} ms") + + +if __name__ == "__main__": + with torch.inference_mode(): + main() diff --git a/lerobot/common/policies/pi0/conversion_scripts/compare_with_jax.py b/lerobot/common/policies/pi0/conversion_scripts/compare_with_jax.py new file mode 100644 index 0000000000000000000000000000000000000000..12df7c395d998e30a2a85ce5f31ec34aff34feaa --- /dev/null +++ b/lerobot/common/policies/pi0/conversion_scripts/compare_with_jax.py @@ -0,0 +1,131 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import pickle +from pathlib import Path + +import torch + +from lerobot.common.datasets.lerobot_dataset import LeRobotDatasetMetadata +from lerobot.common.policies.factory import make_policy +from lerobot.configs.policies import PreTrainedConfig + + +def display(tensor: torch.Tensor): + if tensor.dtype == torch.bool: + tensor = tensor.float() + print(f"Shape: {tensor.shape}") + print(f"Mean: {tensor.mean().item()}") + print(f"Std: {tensor.std().item()}") + print(f"Min: {tensor.min().item()}") + print(f"Max: {tensor.max().item()}") + + +def main(): + num_motors = 14 + device = "cuda" + # model_name = "pi0_aloha_towel" + model_name = "pi0_aloha_sim" + + if model_name == "pi0_aloha_towel": + dataset_repo_id = "lerobot/aloha_static_towel" + else: + dataset_repo_id = "lerobot/aloha_sim_transfer_cube_human" + + ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch" + ckpt_jax_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}" + save_dir = Path(f"../openpi/data/{model_name}/save") + + with open(save_dir / "example.pkl", "rb") as f: + example = pickle.load(f) + with open(save_dir / "outputs.pkl", "rb") as f: + outputs = pickle.load(f) + with open(save_dir / "noise.pkl", "rb") as f: + noise = pickle.load(f) + + with open(ckpt_jax_dir / "assets/norm_stats.json") as f: + norm_stats = json.load(f) + + # Override stats + dataset_meta = LeRobotDatasetMetadata(dataset_repo_id) + dataset_meta.stats["observation.state"]["mean"] = torch.tensor( + norm_stats["norm_stats"]["state"]["mean"][:num_motors], dtype=torch.float32 + ) + dataset_meta.stats["observation.state"]["std"] = torch.tensor( + norm_stats["norm_stats"]["state"]["std"][:num_motors], dtype=torch.float32 + ) + + # Create LeRobot batch from Jax + batch = {} + for cam_key, uint_chw_array in example["images"].items(): + batch[f"observation.images.{cam_key}"] = torch.from_numpy(uint_chw_array) / 255.0 + batch["observation.state"] = torch.from_numpy(example["state"]) + batch["action"] = torch.from_numpy(outputs["actions"]) + batch["task"] = example["prompt"] + + if model_name == "pi0_aloha_towel": + del batch["observation.images.cam_low"] + elif model_name == "pi0_aloha_sim": + batch["observation.images.top"] = batch["observation.images.cam_high"] + del batch["observation.images.cam_high"] + + # Batchify + for key in batch: + if isinstance(batch[key], torch.Tensor): + batch[key] = batch[key].unsqueeze(0) + elif isinstance(batch[key], str): + batch[key] = [batch[key]] + else: + raise ValueError(f"{key}, {batch[key]}") + + # To device + for k in batch: + if isinstance(batch[k], torch.Tensor): + batch[k] = batch[k].to(device=device, dtype=torch.float32) + + noise = torch.from_numpy(noise).to(device=device, dtype=torch.float32) + + from lerobot.common import policies # noqa + + cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir) + cfg.pretrained_path = ckpt_torch_dir + policy = make_policy(cfg, dataset_meta) + + # loss_dict = policy.forward(batch, noise=noise, time=time_beta) + # loss_dict["loss"].backward() + # print("losses") + # display(loss_dict["losses_after_forward"]) + # print("pi_losses") + # display(pi_losses) + + actions = [] + for _ in range(50): + action = policy.select_action(batch, noise=noise) + actions.append(action) + + actions = torch.stack(actions, dim=1) + pi_actions = batch["action"] + print("actions") + display(actions) + print() + print("pi_actions") + display(pi_actions) + print("atol=3e-2", torch.allclose(actions, pi_actions, atol=3e-2)) + print("atol=2e-2", torch.allclose(actions, pi_actions, atol=2e-2)) + print("atol=1e-2", torch.allclose(actions, pi_actions, atol=1e-2)) + + +if __name__ == "__main__": + main() diff --git a/lerobot/common/policies/pi0/conversion_scripts/conversion_utils.py b/lerobot/common/policies/pi0/conversion_scripts/conversion_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..79934151894df5b087afcb2a676af5068f929938 --- /dev/null +++ b/lerobot/common/policies/pi0/conversion_scripts/conversion_utils.py @@ -0,0 +1,84 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from transformers import GemmaConfig, PaliGemmaConfig + + +def get_paligemma_config(precision: str): + config = { + "image_token_index": None, + "pad_token_id": 0, + "bos_token_id": 2, + "eos_token_id": 1, + } + + # image_sizes = {"2b-test": 224, "3b-224px": 224, "3b-448px": 448, "3b-896px": 896} + + image_size = 224 # image_sizes[variant] + patch_size = 14 + num_image_tokens = (image_size**2) // (patch_size**2) + + config["image_token_index"] = 257152 + text_config = { + "vocab_size": 257152, + "num_hidden_layers": 18, + "num_key_value_heads": 1, + "head_dim": 256, + "torch_dtype": precision, + "hidden_size": 2048, + "hidden_activation": "gelu_pytorch_tanh", + "num_attention_heads": 8, + "intermediate_size": 16384, + "is_encoder_decoder": False, + } + vision_config = { + "torch_dtype": precision, + "image_size": image_size, + "patch_size": patch_size, + "num_image_tokens": num_image_tokens, + "hidden_size": 1152, + "intermediate_size": 4304, + "num_hidden_layers": 27, + "num_attention_heads": 16, + "projector_hidden_act": "gelu_fast", + "vision_use_head": False, + } + final_config = PaliGemmaConfig(text_config=text_config, vision_config=vision_config, **config) + return final_config + + +def get_gemma_config(precision: str): + config = { + "image_token_index": None, + "pad_token_id": 0, + "bos_token_id": 2, + "eos_token_id": 1, + } + + config["image_token_index"] = 257152 + text_config = { + "vocab_size": 257152, + "num_hidden_layers": 18, + "num_key_value_heads": 1, + "head_dim": 256, + "torch_dtype": precision, + "hidden_size": 1024, + "hidden_activation": "gelu_pytorch_tanh", + "num_attention_heads": 8, + "intermediate_size": 4096, + "is_encoder_decoder": False, + } + final_config = GemmaConfig() + final_config.update(text_config) + return final_config diff --git a/lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py b/lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py new file mode 100644 index 0000000000000000000000000000000000000000..7f28f4199dedf745772c284b2200167cc0c0ce15 --- /dev/null +++ b/lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py @@ -0,0 +1,437 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Convert pi0 parameters from Jax to Pytorch + +Follow [README of openpi](https://github.com/Physical-Intelligence/openpi) to create a new environment +and install the required libraries. + +```bash +cd ~/code/openpi +source .venv/bin/activate +``` + +Example downloading parameters: +```bash +python +>>> import openpi.shared.download as download +>>> path='s3://openpi-assets/checkpoints/pi0_base/params' +>>> download.maybe_download(path) +``` + +Converting pi0_base: +```python +python lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py \ + --checkpoint_dir /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_base/params \ + --output_path /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_base_pytorch +``` + +```python +python lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py \ + --checkpoint_dir /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim/params \ + --output_path /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim_pytorch +``` +""" + +import argparse +import pathlib + +import jax +import numpy as np +import orbax.checkpoint as ocp +import torch +from jax.sharding import SingleDeviceSharding + +from lerobot.common.policies.pi0.configuration_pi0 import PI0Config +from lerobot.common.policies.pi0.conversion_scripts.conversion_utils import ( + get_gemma_config, + get_paligemma_config, +) +from lerobot.common.policies.pi0.modeling_pi0 import PI0Policy + +PRECISIONS = {"bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16} + + +def slice_paligemma_state_dict(state_dict, config): + suffix = "/value" if "img/embedding/kernel/value" in state_dict else "" + + # fmt: off + # patch embeddings + state_dict["paligemma.vision_tower.vision_model.embeddings.patch_embedding.weight"] = state_dict.pop(f"img/embedding/kernel{suffix}").transpose( + 3, 2, 0, 1 + ) + state_dict["paligemma.vision_tower.vision_model.embeddings.patch_embedding.bias"] = state_dict.pop(f"img/embedding/bias{suffix}") + # positional embeddings + state_dict["paligemma.vision_tower.vision_model.embeddings.position_embedding.weight"] = state_dict.pop(f"img/pos_embedding{suffix}").reshape( + -1, config.vision_config.hidden_size + ) + + # extract vision layers to be sliced at index 0. There are 27 layers in the base model. + encoderblock_layernorm0_scale = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_0/scale{suffix}") + encoderblock_layernorm0_bias = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_0/bias{suffix}") + encoderblock_layernorm1_scale = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_1/scale{suffix}") + encoderblock_layernorm1_bias = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_1/bias{suffix}") + + encoderblock_mlp_dense0_kernel= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_0/kernel{suffix}") + encoderblock_mlp_dense0_bias= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_0/bias{suffix}") + encoderblock_mlp_dense1_kernel= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_1/kernel{suffix}") + encoderblock_mlp_dense1_bias= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_1/bias{suffix}") + + encoderblock_attention_0_key_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/kernel{suffix}") + encoderblock_attention_0_key_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/bias{suffix}") + encoderblock_attention_0_value_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/kernel{suffix}") + encoderblock_attention_0_value_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/bias{suffix}") + encoderblock_attention_0_query_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/kernel{suffix}") + encoderblock_attention_0_query_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/bias{suffix}") + encoderblock_attention_0_out_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/kernel{suffix}") + encoderblock_attention_0_out_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/bias{suffix}") + + for i in range(config.vision_config.num_hidden_layers): + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm1.weight"] = encoderblock_layernorm0_scale[i].transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm1.bias"] = encoderblock_layernorm0_bias[i] + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm2.weight"] = encoderblock_layernorm1_scale[i].transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm2.bias"] = encoderblock_layernorm1_bias[i] + + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.weight"] = encoderblock_mlp_dense0_kernel[i].transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.bias"] = encoderblock_mlp_dense0_bias[i] + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.weight"] = encoderblock_mlp_dense1_kernel[i].transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.bias"] = encoderblock_mlp_dense1_bias[i] + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.weight"] = encoderblock_attention_0_key_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.bias"] = encoderblock_attention_0_key_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.weight"] = encoderblock_attention_0_value_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.bias"] = encoderblock_attention_0_value_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.weight"] = encoderblock_attention_0_query_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.bias"] = encoderblock_attention_0_query_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.weight"] = encoderblock_attention_0_out_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() + state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.bias"] = encoderblock_attention_0_out_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) + + state_dict["paligemma.vision_tower.vision_model.post_layernorm.weight"] = state_dict.pop(f"img/Transformer/encoder_norm/scale{suffix}").transpose() + state_dict["paligemma.vision_tower.vision_model.post_layernorm.bias"] = state_dict.pop(f"img/Transformer/encoder_norm/bias{suffix}") + + # multimodal projector + + state_dict['paligemma.multi_modal_projector.linear.weight'] = state_dict.pop(f"img/head/kernel{suffix}").transpose() + state_dict['paligemma.multi_modal_projector.linear.bias'] = state_dict.pop(f"img/head/bias{suffix}") + + # text decoder (gemma) + embedding_vector = state_dict.pop(f"llm/embedder/input_embedding{suffix}") + state_dict["paligemma.language_model.model.embed_tokens.weight"] = embedding_vector + + # pop the einsum attention + mlp representations. There are 18 layers in gemma-2b. + + llm_attention_attn_vec_einsum = state_dict.pop(f"llm/layers/attn/attn_vec_einsum/w{suffix}") + llm_attention_kv_einsum = state_dict.pop(f"llm/layers/attn/kv_einsum/w{suffix}") + llm_attention_q_einsum = state_dict.pop(f"llm/layers/attn/q_einsum/w{suffix}") + + llm_mlp_gating_einsum = state_dict.pop(f"llm/layers/mlp/gating_einsum{suffix}") + llm_mlp_linear = state_dict.pop(f"llm/layers/mlp/linear{suffix}") + # TODO verify correctness of layer norm loading + + llm_input_layernorm = state_dict.pop(f"llm/layers/pre_attention_norm/scale{suffix}") + llm_post_attention_layernorm = state_dict.pop(f"llm/layers/pre_ffw_norm/scale{suffix}") + + for i in range(config.text_config.num_hidden_layers): + # llm_attention_q_einsum[i].shape = (8, 2048, 256) + q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size) + + state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped + + # llm_attention_kv_einsum[i, 0, 0].shape = (2048, 256) + k_proj_weight_reshaped = llm_attention_kv_einsum[i, 0, 0].transpose() + state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped + # llm_attention_kv_einsum[i, 1, 0].shape = (2048, 256) + v_proj_weight_reshaped = llm_attention_kv_einsum[i, 1, 0].transpose() + state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped + + # output projection. + + # llm_attention_attn_vec_einsum[i].shape = (8, 256, 2048) + o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].transpose(2, 0, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size) + + state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped + # mlp layers + gate_proj_weight = llm_mlp_gating_einsum[i, 0] + state_dict[f"paligemma.language_model.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose() + up_proj_weight = llm_mlp_gating_einsum[i, 1] + state_dict[f"paligemma.language_model.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose() + state_dict[f"paligemma.language_model.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose() + state_dict[f"paligemma.language_model.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i] + state_dict[f"paligemma.language_model.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i] + + state_dict["paligemma.language_model.model.norm.weight"] = state_dict.pop(f"llm/final_norm/scale{suffix}") + state_dict["paligemma.language_model.lm_head.weight"] = embedding_vector # weights are tied. + + # fmt: on + expert_dict = {} + final_state_dict = {} + for key, value in state_dict.items(): + if key not in [ + f"llm/final_norm_1/scale{suffix}", + f"llm/layers/attn/attn_vec_einsum_1/w{suffix}", + f"llm/layers/attn/kv_einsum_1/w{suffix}", + f"llm/layers/attn/q_einsum_1/w{suffix}", + f"llm/layers/mlp_1/gating_einsum{suffix}", + f"llm/layers/mlp_1/linear{suffix}", + f"llm/layers/pre_attention_norm_1/scale{suffix}", + f"llm/layers/pre_ffw_norm_1/scale{suffix}", + ]: + final_state_dict[key] = torch.from_numpy(value) + else: + expert_dict[key] = value + + return final_state_dict, expert_dict + + +def slice_gemma_state_dict(state_dict, config, num_expert=1): + # fmt: off + # text decoder (gemma) + # no embedding vector, the expert just has the decoder layers + + embedding_vector = torch.zeros([config.vocab_size, config.hidden_size]) + state_dict["gemma_expert.model.embed_tokens.weight"] = embedding_vector + + # pop the einsum attention + mlp representations. There are 18 layers in gemma-2b. + + suffix = "/value" if f"llm/layers/attn/attn_vec_einsum_{num_expert}/w/value" in state_dict else "" + + llm_attention_attn_vec_einsum = state_dict.pop(f"llm/layers/attn/attn_vec_einsum_{num_expert}/w{suffix}") + llm_attention_kv_einsum = state_dict.pop(f"llm/layers/attn/kv_einsum_{num_expert}/w{suffix}") + llm_attention_q_einsum = state_dict.pop(f"llm/layers/attn/q_einsum_{num_expert}/w{suffix}") + + llm_mlp_gating_einsum = state_dict.pop(f"llm/layers/mlp_{num_expert}/gating_einsum{suffix}") + llm_mlp_linear = state_dict.pop(f"llm/layers/mlp_{num_expert}/linear{suffix}") + # TODO verify correctness of layer norm loading + + llm_input_layernorm = state_dict.pop(f"llm/layers/pre_attention_norm_{num_expert}/scale{suffix}") + llm_post_attention_layernorm = state_dict.pop(f"llm/layers/pre_ffw_norm_{num_expert}/scale{suffix}") + + for i in range(config.num_hidden_layers): + q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.num_attention_heads * config.head_dim, config.hidden_size) + + state_dict[f"gemma_expert.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped + + k_proj_weight_reshaped = llm_attention_kv_einsum[i, 0, 0].transpose() + state_dict[f"gemma_expert.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped + v_proj_weight_reshaped = llm_attention_kv_einsum[i, 1, 0].transpose() + state_dict[f"gemma_expert.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped + + # output projection. + + # llm_attention_attn_vec_einsum[i].shape = (8, 256, 1024) + o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].reshape(config.num_attention_heads * config.head_dim, config.hidden_size).transpose(1,0)# .transpose(2, 0, 1).reshape(config.num_attention_heads * config.head_dim, config.hidden_size).transpose(1, 0) + + state_dict[f"gemma_expert.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped + # mlp layers + gate_proj_weight = llm_mlp_gating_einsum[i, 0] + state_dict[f"gemma_expert.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose() + up_proj_weight = llm_mlp_gating_einsum[i, 1] + state_dict[f"gemma_expert.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose() + state_dict[f"gemma_expert.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose() + state_dict[f"gemma_expert.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i] + state_dict[f"gemma_expert.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i] + + state_dict["gemma_expert.model.norm.weight"] = state_dict.pop(f"llm/final_norm_{num_expert}/scale{suffix}") + state_dict["gemma_expert.lm_head.weight"] = embedding_vector # weights are tied. (and zeros here) + + # fmt: on + final_state_dict = {} + for key, value in state_dict.items(): + if not isinstance(value, torch.Tensor): + final_state_dict[key] = torch.from_numpy(value) + else: + final_state_dict[key] = value + return final_state_dict + + +def flatten_for_memory(tree, parent_key=""): + out = {} + for k, v in tree.items(): + new_key = f"{parent_key}/{k}" if parent_key else k + if isinstance(v, dict): + out.update(flatten_for_memory(v, new_key)) + else: + out[new_key] = np.array(v) # Ensure conversion to np.array for consistency + return out + + +def flatten_for_npz(tree, parent_key=""): + out = {} + for k, v in tree.items(): + new_key = f"{parent_key}/{k}" if parent_key else k + if isinstance(v, dict): + out.update(flatten_for_npz(v, new_key)) + else: + # bf16/f32 here? + out[new_key] = np.array(v) + return out + + +def slice_initial_orbax_checkpoint(checkpoint_dir: str): + params_path = pathlib.Path(checkpoint_dir).resolve() + checkpointer = ocp.PyTreeCheckpointer() + + metadata = checkpointer.metadata(params_path) + print("Metadata keys:", list(metadata.keys())) + + params_name = "params" + + item = {params_name: metadata[params_name]} + device = jax.local_devices()[0] # Use the first local device + sharding = SingleDeviceSharding(device) + restored = checkpointer.restore( + params_path, + ocp.args.PyTreeRestore( + item=item, + restore_args=jax.tree_util.tree_map( + lambda _: ocp.ArrayRestoreArgs( + restore_type=jax.Array, # or np.ndarray, but bf16 is annoying about it + sharding=sharding, + ), + item, + ), + transforms={}, + ), + ) + params = restored[params_name] + + # get params for PaliGemma + pali_params = params["PaliGemma"] + del params["PaliGemma"] + pali_params_flat = flatten_for_npz(pali_params) + return {"paligemma_params": pali_params_flat, "projection_params": params} + + +def update_keys_with_prefix(d: dict, prefix: str) -> dict: + """Update dictionary keys by adding a prefix.""" + return {f"{prefix}{key}": value for key, value in d.items()} + + +def convert_pi0_checkpoint(checkpoint_dir: str, precision: str, tokenizer_id: str, output_path: str): + # Break down orbax ckpts - they are in OCDBT + initial_params = slice_initial_orbax_checkpoint(checkpoint_dir=checkpoint_dir) + # process projection params + keys = [ + "state_proj", + "action_in_proj", + "action_out_proj", + "action_time_mlp_in", + "action_time_mlp_out", + ] + + projection_params = {} + for key in keys: + kernel_params = initial_params["projection_params"][key]["kernel"] + bias_params = initial_params["projection_params"][key]["bias"] + if isinstance(kernel_params, dict): + weight = kernel_params["value"] + bias = bias_params["value"] + else: + weight = kernel_params + bias = bias_params + projection_params[f"{key}.weight"] = torch.from_numpy(np.array(weight)).T + projection_params[f"{key}.bias"] = torch.from_numpy(np.array(bias)) + + # Process PaliGemma weights + paligemma_config = get_paligemma_config(precision) + paligemma_params, gemma_raw_dictionary = slice_paligemma_state_dict( + initial_params["paligemma_params"], paligemma_config + ) + + # Process Gemma weights (at this stage they are unused) + gemma_config = get_gemma_config(precision) + gemma_params = slice_gemma_state_dict(gemma_raw_dictionary, config=gemma_config) + + # Instantiate model from configs + + if "pi0_aloha_sim" in checkpoint_dir: + pi0_config = PI0Config( + empty_cameras=2, + adapt_to_pi_aloha=True, + use_delta_joint_actions_aloha=False, + ) + elif "pi0_aloha_towel" in checkpoint_dir: + pi0_config = PI0Config( + adapt_to_pi_aloha=True, + use_delta_joint_actions_aloha=True, + ) + elif "pi0_base" in checkpoint_dir: + pi0_config = PI0Config( + empty_cameras=0, + adapt_to_pi_aloha=False, + use_delta_joint_actions_aloha=False, + ) + else: + raise ValueError() + + # gemma_config=gemma_config, paligemma_config=paligemma_config) + pi0_model = PI0Policy(pi0_config) + + paligemma_params = update_keys_with_prefix(paligemma_params, "model.paligemma_with_expert.") + gemma_params = update_keys_with_prefix(gemma_params, "model.paligemma_with_expert.") + projection_params = update_keys_with_prefix(projection_params, "model.") + + # load state dict + torch_dtype = PRECISIONS[precision] + pi0_model.load_state_dict({**paligemma_params, **gemma_params, **projection_params}) + pi0_model = pi0_model.to(torch_dtype) + # pi0_tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) + + pi0_model.save_pretrained(output_path, safe_serialization=True) + # pi0_tokenizer.save_pretrained(output_path, dtype=torch_dtype) + + # assert that model loads properly + del pi0_model + PI0Policy.from_pretrained(output_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--checkpoint_dir", + default="/raid/pablo/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim/params", + type=str, + help="Path to the ocdbt checkpoint", + ) + + parser.add_argument( + "--precision", + choices=["float32", "bfloat16", "float16"], + default="float32", + type=str, + help="Precision identifier for model conversion - should match the base checkpoint precision.", + ) + # tokenizer is identical to paligemma, it appears + + parser.add_argument( + "--tokenizer_hub_id", + default="google/paligemma-3b-pt-224", + type=str, + help="Hub path to the tokenizer to save", + ) + + parser.add_argument( + "--output_path", + required=True, + type=str, + help="Path to save converted weights to", + ) + + args = parser.parse_args() + convert_pi0_checkpoint( + checkpoint_dir=args.checkpoint_dir, + precision=args.precision, + tokenizer_id=args.tokenizer_hub_id, + output_path=args.output_path, + ) diff --git a/lerobot/common/policies/pi0/flex_attention.py b/lerobot/common/policies/pi0/flex_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..61b4a7867967deffa07487bec0aa92b1d8edfe42 --- /dev/null +++ b/lerobot/common/policies/pi0/flex_attention.py @@ -0,0 +1,141 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F # noqa: N812 +from packaging.version import Version + +if Version(torch.__version__) > Version("2.5.0"): + # Ffex attention is only available from torch 2.5 onwards + from torch.nn.attention.flex_attention import ( + _mask_mod_signature, + _round_up_to_multiple, + create_block_mask, + create_mask, + flex_attention, + ) + + +# @torch.compile(dynamic=False) +def flex_attention_forward( + attention_mask: torch.Tensor, + batch_size: int, + head_dim: int, + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + scaling=None, +): + """ + This is defined out of classes to make compile happy. + """ + + original_dtype = query_states.dtype + num_att_heads = 8 + num_key_value_heads = 1 + num_key_value_groups = num_att_heads // num_key_value_heads + + key_states = key_states[:, :, :, None, :] + key_states = key_states.expand( + batch_size, key_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim + ) + key_states = key_states.reshape( + batch_size, key_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim + ) + + value_states = value_states[:, :, :, None, :] + value_states = value_states.expand( + batch_size, value_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim + ) + value_states = value_states.reshape( + batch_size, value_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim + ) + + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + query_states = query_states.to(torch.float32) + key_states = key_states.to(torch.float32) + value_states = value_states.to(torch.float32) + + causal_mask = attention_mask + if causal_mask is not None: + causal_mask = causal_mask[:, None, :, : key_states.shape[2]] + + if causal_mask.shape[1] == 1 and query_states.shape[1] > 1: + causal_mask = causal_mask.expand(-1, query_states.shape[1], -1, -1) + + def precomputed_mask_factory(precomputed_mask: torch.Tensor) -> _mask_mod_signature: + def mask_mod(b, h, q_idx, kv_idx): + # Danger zone: if b,h,q_idx,kv_idx exceed the shape, device-side assert occurs. + return precomputed_mask[b][h][q_idx][kv_idx] + + return mask_mod + + b_mask, h_mask, q_len, kv_len = causal_mask.shape # The shape of your mask + + block_size = 128 + q_len_rounded = _round_up_to_multiple(q_len, block_size) + kv_len_rounded = _round_up_to_multiple(kv_len, block_size) + + # *CRITICAL* we do need to expand here, else we get a CUDA index error + + pad_q = q_len_rounded - q_len + pad_k = kv_len_rounded - kv_len + + padded_causal_mask = F.pad(causal_mask, (0, pad_k, 0, pad_q), value=0.0) + mask_mod_fn_orig = precomputed_mask_factory(padded_causal_mask) + + mask_4d = create_mask( + mod_fn=mask_mod_fn_orig, + B=b_mask, + H=h_mask, + Q_LEN=q_len_rounded, + KV_LEN=kv_len_rounded, + device=causal_mask.device, + _compile=False, + ) + + mask_mod_fn_padded = precomputed_mask_factory(mask_4d) + block_mask = create_block_mask( + mask_mod=mask_mod_fn_padded, + B=b_mask, + H=h_mask, + Q_LEN=q_len_rounded, + KV_LEN=kv_len_rounded, + BLOCK_SIZE=block_size, + device=causal_mask.device, + _compile=False, + ) + + # mask is applied inside the kernel, ideally more efficiently than score_mod. + attn_output, attention_weights = flex_attention( + query_states, + key_states, + value_states, + block_mask=block_mask, + enable_gqa=True, # because we shaped query/key states for GQA + scale=head_dim**-0.5 if scaling is None else scaling, + return_lse=True, + ) + + attn_output = attn_output.to(dtype=original_dtype) + attn_output = attn_output.transpose(1, 2).contiguous() # [B, Q_LEN, H, head_dim] + attn_output = attn_output.reshape( + batch_size, + -1, + attn_output.shape[2] * attn_output.shape[3], # merges [H, head_dim] + ) + return attn_output diff --git a/lerobot/common/policies/pi0/modeling_pi0.py b/lerobot/common/policies/pi0/modeling_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..90587d368359cb700c624e4178fb0e054e6455f1 --- /dev/null +++ b/lerobot/common/policies/pi0/modeling_pi0.py @@ -0,0 +1,732 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +π0: A Vision-Language-Action Flow Model for General Robot Control + +[Paper](https://www.physicalintelligence.company/download/pi0.pdf) +[Jax code](https://github.com/Physical-Intelligence/openpi) + +Designed by Physical Intelligence. Ported from Jax by Hugging Face. + +Install pi0 extra dependencies: +```bash +pip install -e ".[pi0]" +``` + +Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`): +```bash +python lerobot/scripts/train.py \ +--policy.path=lerobot/pi0 \ +--dataset.repo_id=danaaubakirova/koch_test +``` + +Example of finetuning the pi0 neural network with PaliGemma and expert Gemma +pretrained with VLM default parameters before pi0 finetuning: +```bash +python lerobot/scripts/train.py \ +--policy.type=pi0 \ +--dataset.repo_id=danaaubakirova/koch_test +``` + +Example of using the pi0 pretrained model outside LeRobot training framework: +```python +policy = Pi0Policy.from_pretrained("lerobot/pi0") +``` + +""" + +import math +from collections import deque + +import torch +import torch.nn.functional as F # noqa: N812 +from torch import Tensor, nn +from transformers import AutoTokenizer + +from lerobot.common.constants import ACTION, OBS_STATE +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pi0.configuration_pi0 import PI0Config +from lerobot.common.policies.pi0.paligemma_with_expert import ( + PaliGemmaWithExpertConfig, + PaliGemmaWithExpertModel, +) +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.utils.utils import get_safe_dtype + + +def create_sinusoidal_pos_embedding( + time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu" +) -> Tensor: + """Computes sine-cosine positional embedding vectors for scalar positions.""" + if dimension % 2 != 0: + raise ValueError(f"dimension ({dimension}) must be divisible by 2") + + if time.ndim != 1: + raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.") + + dtype = get_safe_dtype(torch.float64, device.type) + fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device) + period = min_period * (max_period / min_period) ** fraction + + # Compute the outer product + scaling_factor = 1.0 / period * 2 * math.pi + sin_input = scaling_factor[None, :] * time[:, None] + pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1) + return pos_emb + + +def sample_beta(alpha, beta, bsize, device): + gamma1 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / alpha) + gamma2 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / beta) + return gamma1 / (gamma1 + gamma2) + + +def make_att_2d_masks(pad_masks, att_masks): + """Copied from big_vision. + + Tokens can attend to valid inputs tokens which have a cumulative mask_ar + smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to + setup several types of attention, for example: + + [[1 1 1 1 1 1]]: pure causal attention. + + [[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between + themselves and the last 3 tokens have a causal attention. The first + entry could also be a 1 without changing behaviour. + + [[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a + block can attend all previous blocks and all tokens on the same block. + + Args: + input_mask: bool[B, N] true if its part of the input, false if padding. + mask_ar: int32[B, N] mask that's 1 where previous tokens cannot depend on + it and 0 where it shares the same attention mask as the previous token. + """ + if att_masks.ndim != 2: + raise ValueError(att_masks.ndim) + if pad_masks.ndim != 2: + raise ValueError(pad_masks.ndim) + + cumsum = torch.cumsum(att_masks, dim=1) + att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None] + pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None] + att_2d_masks = att_2d_masks & pad_2d_masks + return att_2d_masks + + +def resize_with_pad(img, width, height, pad_value=-1): + # assume no-op when width height fits already + if img.ndim != 4: + raise ValueError(f"(b,c,h,w) expected, but {img.shape}") + + cur_height, cur_width = img.shape[2:] + + ratio = max(cur_width / width, cur_height / height) + resized_height = int(cur_height / ratio) + resized_width = int(cur_width / ratio) + resized_img = F.interpolate( + img, size=(resized_height, resized_width), mode="bilinear", align_corners=False + ) + + pad_height = max(0, int(height - resized_height)) + pad_width = max(0, int(width - resized_width)) + + # pad on left and top of image + padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value) + return padded_img + + +def pad_vector(vector, new_dim): + """Can be (batch_size x sequence_length x features_dimension) + or (batch_size x features_dimension) + """ + if vector.shape[-1] == new_dim: + return vector + shape = list(vector.shape) + current_dim = shape[-1] + shape[-1] = new_dim + new_vector = torch.zeros(*shape, dtype=vector.dtype, device=vector.device) + new_vector[..., :current_dim] = vector + return new_vector + + +def normalize(x, min_val, max_val): + return (x - min_val) / (max_val - min_val) + + +def unnormalize(x, min_val, max_val): + return x * (max_val - min_val) + min_val + + +def safe_arcsin(value): + # This ensures that the input stays within + # [−1,1] to avoid invalid values for arcsin + return torch.arcsin(torch.clamp(value, -1.0, 1.0)) + + +def aloha_gripper_to_angular(value): + # Aloha transforms the gripper positions into a linear space. The following code + # reverses this transformation to be consistent with pi0 which is pretrained in + # angular space. + # + # These values are coming from the Aloha code: + # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED + value = unnormalize(value, min_val=0.01844, max_val=0.05800) + + # This is the inverse of the angular to linear transformation inside the Interbotix code. + def linear_to_radian(linear_position, arm_length, horn_radius): + value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position) + return safe_arcsin(value) + + # The constants are taken from the Interbotix code. + value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022) + + # Normalize to [0, 1]. + # The values 0.4 and 1.5 were measured on an actual Trossen robot. + return normalize(value, min_val=0.4, max_val=1.5) + + +def aloha_gripper_from_angular(value): + # Convert from the gripper position used by pi0 to the gripper position that is used by Aloha. + # Note that the units are still angular but the range is different. + + # The values 0.4 and 1.5 were measured on an actual Trossen robot. + value = unnormalize(value, min_val=0.4, max_val=1.5) + + # These values are coming from the Aloha code: + # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE + return normalize(value, min_val=-0.6213, max_val=1.4910) + + +def aloha_gripper_from_angular_inv(value): + # Directly inverts the gripper_from_angular function. + value = unnormalize(value, min_val=-0.6213, max_val=1.4910) + return normalize(value, min_val=0.4, max_val=1.5) + + +class PI0Policy(PreTrainedPolicy): + """Wrapper class around PI0FlowMatching model to train and run inference within LeRobot.""" + + config_class = PI0Config + name = "pi0" + + def __init__( + self, + config: PI0Config, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + + super().__init__(config) + config.validate_features() + self.config = config + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + self.language_tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224") + self.model = PI0FlowMatching(config) + + self.reset() + + def reset(self): + """This should be called whenever the environment is reset.""" + self._action_queue = deque([], maxlen=self.config.n_action_steps) + + def get_optim_params(self) -> dict: + return self.parameters() + + @torch.no_grad + def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor: + """Select a single action given environment observations. + + This method wraps `select_actions` in order to return one action at a time for execution in the + environment. It works by managing the actions in a queue and only calling `select_actions` when the + queue is empty. + """ + self.eval() + + if self.config.adapt_to_pi_aloha: + batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + + batch = self.normalize_inputs(batch) + + # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by + # querying the policy. + if len(self._action_queue) == 0: + images, img_masks = self.prepare_images(batch) + state = self.prepare_state(batch) + lang_tokens, lang_masks = self.prepare_language(batch) + + actions = self.model.sample_actions( + images, img_masks, lang_tokens, lang_masks, state, noise=noise + ) + + # Unpad actions + original_action_dim = self.config.action_feature.shape[0] + actions = actions[:, :, :original_action_dim] + + actions = self.unnormalize_outputs({"action": actions})["action"] + + if self.config.adapt_to_pi_aloha: + actions = self._pi_aloha_encode_actions(actions) + + # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue + # effectively has shape (n_action_steps, batch_size, *), hence the transpose. + self._action_queue.extend(actions.transpose(0, 1)) + return self._action_queue.popleft() + + def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> tuple[Tensor, dict[str, Tensor]]: + """Do a full training forward pass to compute the loss""" + if self.config.adapt_to_pi_aloha: + batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION]) + + batch = self.normalize_inputs(batch) + batch = self.normalize_targets(batch) + + images, img_masks = self.prepare_images(batch) + state = self.prepare_state(batch) + lang_tokens, lang_masks = self.prepare_language(batch) + actions = self.prepare_action(batch) + actions_is_pad = batch.get("action_is_pad") + + loss_dict = {} + losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time) + loss_dict["losses_after_forward"] = losses.clone() + + if actions_is_pad is not None: + in_episode_bound = ~actions_is_pad + losses = losses * in_episode_bound.unsqueeze(-1) + loss_dict["losses_after_in_ep_bound"] = losses.clone() + + # Remove padding + losses = losses[:, :, : self.config.max_action_dim] + loss_dict["losses_after_rm_padding"] = losses.clone() + + # For backward pass + loss = losses.mean() + # For logging + loss_dict["l2_loss"] = loss.item() + + return loss, loss_dict + + def prepare_images(self, batch): + """Apply Pi0 preprocessing to the images, like resizing to 224x224 and padding to keep aspect ratio, and + convert pixel range from [0.0, 1.0] to [-1.0, 1.0] as requested by SigLIP. + """ + images = [] + img_masks = [] + + present_img_keys = [key for key in self.config.image_features if key in batch] + missing_img_keys = [key for key in self.config.image_features if key not in batch] + + if len(present_img_keys) == 0: + raise ValueError( + f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})" + ) + + # Preprocess image features present in the batch + for key in present_img_keys: + img = batch[key] + + if self.config.resize_imgs_with_padding is not None: + img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0) + + # Normalize from range [0,1] to [-1,1] as expected by siglip + img = img * 2.0 - 1.0 + + bsize = img.shape[0] + device = img.device + mask = torch.ones(bsize, dtype=torch.bool, device=device) + images.append(img) + img_masks.append(mask) + + # Create image features not present in the batch + # as fully 0 padded images. + for num_empty_cameras in range(len(missing_img_keys)): + if num_empty_cameras >= self.config.empty_cameras: + break + img = torch.ones_like(img) * -1 + mask = torch.zeros_like(mask) + images.append(img) + img_masks.append(mask) + + return images, img_masks + + def prepare_language(self, batch) -> tuple[Tensor, Tensor]: + """Tokenize the text input""" + device = batch[OBS_STATE].device + tasks = batch["task"] + + # PaliGemma prompt has to end with a new line + tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks] + + tokenized_prompt = self.language_tokenizer.__call__( + tasks, + padding="max_length", + padding_side="right", + max_length=self.config.tokenizer_max_length, + return_tensors="pt", + ) + lang_tokens = tokenized_prompt["input_ids"].to(device=device) + lang_masks = tokenized_prompt["attention_mask"].to(device=device, dtype=torch.bool) + + return lang_tokens, lang_masks + + def _pi_aloha_decode_state(self, state): + # Flip the joints. + for motor_idx in [1, 2, 8, 9]: + state[:, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx]) + return state + + def _pi_aloha_encode_actions(self, actions): + # Flip the joints. + for motor_idx in [1, 2, 8, 9]: + actions[:, :, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx]) + return actions + + def _pi_aloha_encode_actions_inv(self, actions): + # Flip the joints again. + for motor_idx in [1, 2, 8, 9]: + actions[:, :, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx]) + return actions + + def prepare_state(self, batch): + """Pad state""" + state = pad_vector(batch[OBS_STATE], self.config.max_state_dim) + return state + + def prepare_action(self, batch): + """Pad action""" + actions = pad_vector(batch[ACTION], self.config.max_action_dim) + return actions + + +class PI0FlowMatching(nn.Module): + """ + π0: A Vision-Language-Action Flow Model for General Robot Control + + [Paper](https://www.physicalintelligence.company/download/pi0.pdf) + [Jax code](https://github.com/Physical-Intelligence/openpi) + + Designed by Physical Intelligence. Ported from Jax by Hugging Face. + ┌──────────────────────────────┐ + │ actions │ + │ ▲ │ + │ ┌┴─────┐ │ + │ kv cache │Gemma │ │ + │ ┌──────────►│Expert│ │ + │ │ │ │ │ + │ ┌┴────────┐ │x 10 │ │ + │ │ │ └▲──▲──┘ │ + │ │PaliGemma│ │ │ │ + │ │ │ │ robot state │ + │ │ │ noise │ + │ └▲──▲─────┘ │ + │ │ │ │ + │ │ image(s) │ + │ language tokens │ + └──────────────────────────────┘ + """ + + def __init__(self, config): + super().__init__() + self.config = config + + paligemma_with_export_config = PaliGemmaWithExpertConfig( + freeze_vision_encoder=self.config.freeze_vision_encoder, + train_expert_only=self.config.train_expert_only, + attention_implementation=self.config.attention_implementation, + ) + self.paligemma_with_expert = PaliGemmaWithExpertModel(paligemma_with_export_config) + + # Projections are float32 + self.state_proj = nn.Linear(self.config.max_state_dim, self.config.proj_width) + self.action_in_proj = nn.Linear(self.config.max_action_dim, self.config.proj_width) + self.action_out_proj = nn.Linear(self.config.proj_width, self.config.max_action_dim) + + self.action_time_mlp_in = nn.Linear(self.config.proj_width * 2, self.config.proj_width) + self.action_time_mlp_out = nn.Linear(self.config.proj_width, self.config.proj_width) + + self.set_requires_grad() + + def set_requires_grad(self): + for params in self.state_proj.parameters(): + params.requires_grad = self.config.train_state_proj + + def sample_noise(self, shape, device): + noise = torch.normal( + mean=0.0, + std=1.0, + size=shape, + dtype=torch.float32, + device=device, + ) + return noise + + def sample_time(self, bsize, device): + time_beta = sample_beta(1.5, 1.0, bsize, device) + time = time_beta * 0.999 + 0.001 + return time.to(dtype=torch.float32, device=device) + + def embed_prefix( + self, images, img_masks, lang_tokens, lang_masks + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Embed images with SigLIP and language tokens with embedding layer to prepare + for PaliGemma transformer processing. + """ + # TODO: avoid list in python and torch.cat ; prefer pre-allocation with torch.empty + embs = [] + pad_masks = [] + att_masks = [] + + # TODO: remove for loop + for ( + img, + img_mask, + ) in zip(images, img_masks, strict=False): + img_emb = self.paligemma_with_expert.embed_image(img) + img_emb = img_emb.to(dtype=torch.bfloat16) + + # Normalize image embeddings + img_emb_dim = img_emb.shape[-1] + img_emb = img_emb * torch.tensor(img_emb_dim**0.5, dtype=img_emb.dtype, device=img_emb.device) + + bsize, num_img_embs = img_emb.shape[:2] + img_mask = img_mask[:, None].expand(bsize, num_img_embs) + + embs.append(img_emb) + pad_masks.append(img_mask) + + # Create attention masks so that image tokens attend to each other + att_masks += [0] * num_img_embs + + lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens) + + # Normalize language embeddings + lang_emb_dim = lang_emb.shape[-1] + lang_emb = lang_emb * math.sqrt(lang_emb_dim) + + embs.append(lang_emb) + pad_masks.append(lang_masks) + + # full attention between image and language inputs + num_lang_embs = lang_emb.shape[1] + att_masks += [0] * num_lang_embs + + embs = torch.cat(embs, dim=1) + pad_masks = torch.cat(pad_masks, dim=1) + att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device) + att_masks = att_masks[None, :].expand(bsize, len(att_masks)) + + return embs, pad_masks, att_masks + + def embed_suffix(self, state, noisy_actions, timestep): + """Embed state, noisy_actions, timestep to prepare for Expert Gemma processing.""" + embs = [] + pad_masks = [] + att_masks = [] + + # Embed state + state_emb = self.state_proj(state) + state_emb = state_emb.to(dtype=torch.bfloat16) + embs.append(state_emb[:, None, :]) + bsize = state_emb.shape[0] + dtype = state_emb.dtype + device = state_emb.device + + state_mask = torch.ones(bsize, 1, dtype=torch.bool, device=device) + pad_masks.append(state_mask) + + # Set attention masks so that image and language inputs do not attend to state or actions + att_masks += [1] + + # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1] + time_emb = create_sinusoidal_pos_embedding( + timestep, self.config.proj_width, min_period=4e-3, max_period=4.0, device=device + ) + time_emb = time_emb.type(dtype=dtype) + + # Fuse timestep + action information using an MLP + action_emb = self.action_in_proj(noisy_actions) + + time_emb = time_emb[:, None, :].expand_as(action_emb) + action_time_emb = torch.cat([action_emb, time_emb], dim=2) + + action_time_emb = self.action_time_mlp_in(action_time_emb) + action_time_emb = F.silu(action_time_emb) # swish == silu + action_time_emb = self.action_time_mlp_out(action_time_emb) + + # Add to input tokens + embs.append(action_time_emb) + + bsize, action_time_dim = action_time_emb.shape[:2] + action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=device) + pad_masks.append(action_time_mask) + + # Set attention masks so that image, language and state inputs do not attend to action tokens + att_masks += [1] + ([0] * (self.config.n_action_steps - 1)) + + embs = torch.cat(embs, dim=1) + pad_masks = torch.cat(pad_masks, dim=1) + att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device) + att_masks = att_masks[None, :].expand(bsize, len(att_masks)) + + return embs, pad_masks, att_masks + + def forward( + self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None + ) -> Tensor: + """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)""" + if noise is None: + noise = self.sample_noise(actions.shape, actions.device) + + if time is None: + time = self.sample_time(actions.shape[0], actions.device) + + time_expanded = time[:, None, None] + x_t = time_expanded * noise + (1 - time_expanded) * actions + u_t = noise - actions + + prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix( + images, img_masks, lang_tokens, lang_masks + ) + suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, time) + + pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1) + att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1) + + att_2d_masks = make_att_2d_masks(pad_masks, att_masks) + position_ids = torch.cumsum(pad_masks, dim=1) - 1 + + (_, suffix_out), _ = self.paligemma_with_expert.forward( + attention_mask=att_2d_masks, + position_ids=position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, suffix_embs], + use_cache=False, + fill_kv_cache=False, + ) + suffix_out = suffix_out[:, -self.config.n_action_steps :] + # Original openpi code, upcast attention output + suffix_out = suffix_out.to(dtype=torch.float32) + v_t = self.action_out_proj(suffix_out) + + losses = F.mse_loss(u_t, v_t, reduction="none") + return losses + + def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, noise=None) -> Tensor: + """Do a full inference forward and compute the action (batch_size x num_steps x num_motors)""" + bsize = state.shape[0] + device = state.device + + if noise is None: + actions_shape = (bsize, self.config.n_action_steps, self.config.max_action_dim) + noise = self.sample_noise(actions_shape, device) + + prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix( + images, img_masks, lang_tokens, lang_masks + ) + prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks) + prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1 + + # Compute image and language key value cache + _, past_key_values = self.paligemma_with_expert.forward( + attention_mask=prefix_att_2d_masks, + position_ids=prefix_position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, None], + use_cache=self.config.use_cache, + fill_kv_cache=True, + ) + + dt = -1.0 / self.config.num_steps + dt = torch.tensor(dt, dtype=torch.float32, device=device) + + x_t = noise + time = torch.tensor(1.0, dtype=torch.float32, device=device) + while time >= -dt / 2: + expanded_time = time.expand(bsize) + v_t = self.denoise_step( + state, + prefix_pad_masks, + past_key_values, + x_t, + expanded_time, + ) + + # Euler step + x_t += dt * v_t + time += dt + return x_t + + def denoise_step( + self, + state, + prefix_pad_masks, + past_key_values, + x_t, + timestep, + ): + """Apply one denoising step of the noise `x_t` at a given timestep.""" + suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, timestep) + + suffix_len = suffix_pad_masks.shape[1] + batch_size = prefix_pad_masks.shape[0] + prefix_len = prefix_pad_masks.shape[1] + prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len) + + suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks) + + full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2) + + prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None] + position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1 + + outputs_embeds, _ = self.paligemma_with_expert.forward( + attention_mask=full_att_2d_masks, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=[None, suffix_embs], + use_cache=self.config.use_cache, + fill_kv_cache=False, + ) + suffix_out = outputs_embeds[1] + suffix_out = suffix_out[:, -self.config.n_action_steps :] + suffix_out = suffix_out.to(dtype=torch.float32) + v_t = self.action_out_proj(suffix_out) + return v_t diff --git a/lerobot/common/policies/pi0/paligemma_with_expert.py b/lerobot/common/policies/pi0/paligemma_with_expert.py new file mode 100644 index 0000000000000000000000000000000000000000..2770fc100e28b9b13b592fe02a470165075874e6 --- /dev/null +++ b/lerobot/common/policies/pi0/paligemma_with_expert.py @@ -0,0 +1,421 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional, Union + +import torch +import torch.version +from pytest import Cache +from torch import nn +from transformers import ( + AutoConfig, + GemmaForCausalLM, + PaliGemmaForConditionalGeneration, + PretrainedConfig, + PreTrainedModel, +) +from transformers.models.auto import CONFIG_MAPPING + +from lerobot.common.policies.pi0.flex_attention import flex_attention_forward + + +def apply_rope(x, positions, max_wavelength=10_000): + """ + Applies RoPE positions [B, L] to x [B, L, H, D]. + """ + d_half = x.shape[-1] // 2 + device = x.device + dtype = x.dtype + x = x.to(torch.float32) + + freq_exponents = (2.0 / x.shape[-1]) * torch.arange(d_half, dtype=torch.float32, device=device) + timescale = max_wavelength**freq_exponents + radians = positions[..., None].to(torch.float32) / timescale[None, None, :].to(torch.float32) + + radians = radians[..., None, :] + + sin = torch.sin(radians) # .to(dtype=dtype) + cos = torch.cos(radians) # .to(dtype=dtype) + + x1, x2 = x.split(d_half, dim=-1) + res = torch.empty_like(x) + res[..., :d_half] = x1 * cos - x2 * sin + res[..., d_half:] = x2 * cos + x1 * sin + + return res.to(dtype) + + +class PaliGemmaWithExpertConfig(PretrainedConfig): + model_type = "PaliGemmaWithExpertModel" + sub_configs = {"paligemma_config": AutoConfig, "gemma_expert_config": AutoConfig} + + def __init__( + self, + paligemma_config: dict | None = None, + gemma_expert_config: dict | None = None, + freeze_vision_encoder: bool = True, + train_expert_only: bool = True, + attention_implementation: str = "eager", + **kwargs, + ): + self.freeze_vision_encoder = freeze_vision_encoder + self.train_expert_only = train_expert_only + self.attention_implementation = attention_implementation + + if paligemma_config is None: + # Default config from Pi0 + self.paligemma_config = CONFIG_MAPPING["paligemma"]( + transformers_version="4.48.1", + _vocab_size=257152, + bos_token_id=2, + eos_token_id=1, + hidden_size=2048, + image_token_index=257152, + model_type="paligemma", + pad_token_id=0, + projection_dim=2048, + text_config={ + "hidden_activation": "gelu_pytorch_tanh", + "hidden_size": 2048, + "intermediate_size": 16384, + "model_type": "gemma", + "num_attention_heads": 8, + "num_hidden_layers": 18, + "num_image_tokens": 256, + "num_key_value_heads": 1, + "torch_dtype": "float32", + "vocab_size": 257152, + }, + vision_config={ + "hidden_size": 1152, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "num_image_tokens": 256, + "patch_size": 14, + "projection_dim": 2048, + "projector_hidden_act": "gelu_fast", + "torch_dtype": "float32", + "vision_use_head": False, + }, + ) + elif isinstance(self.paligemma_config, dict): + # Override Pi0 default config for PaliGemma + if "model_type" not in gemma_expert_config: + paligemma_config["model_type"] = "paligemma" + + cfg_cls = CONFIG_MAPPING[paligemma_config["model_type"]] + self.paligemma_config = cfg_cls(**paligemma_config) + + if gemma_expert_config is None: + # Default config from Pi0 + self.gemma_expert_config = CONFIG_MAPPING["gemma"]( + attention_bias=False, + attention_dropout=0.0, + bos_token_id=2, + eos_token_id=1, + head_dim=256, + hidden_act="gelu_pytorch_tanh", + hidden_activation="gelu_pytorch_tanh", + hidden_size=1024, + initializer_range=0.02, + intermediate_size=4096, + max_position_embeddings=8192, + model_type="gemma", + num_attention_heads=8, + num_hidden_layers=18, + num_key_value_heads=1, + pad_token_id=0, + rms_norm_eps=1e-06, + rope_theta=10000.0, + torch_dtype="float32", + transformers_version="4.48.1", + use_cache=True, + vocab_size=257152, + ) + elif isinstance(self.gemma_expert_config, dict): + # Override Pi0 default config for Gemma Expert + if "model_type" not in gemma_expert_config: + gemma_expert_config["model_type"] = "gemma" + + cfg_cls = CONFIG_MAPPING[paligemma_config["model_type"]] + self.gemma_expert_config = cfg_cls(**gemma_expert_config) + + super().__init__(**kwargs) + + def __post_init__(self): + super().__post_init__() + if self.train_expert_only and not self.freeze_vision_encoder: + raise ValueError( + "You set `freeze_vision_encoder=False` and `train_expert_only=True` which are not compatible." + ) + + if self.attention_implementation not in ["eager", "fa2", "flex"]: + raise ValueError( + f"Wrong value provided for `attention_implementation` ({self.attention_implementation}). Expected 'eager', 'fa2' or 'flex'." + ) + + +class PaliGemmaWithExpertModel(PreTrainedModel): + config_class = PaliGemmaWithExpertConfig + + def __init__(self, config: PaliGemmaWithExpertConfig): + super().__init__(config=config) + self.config = config + self.paligemma = PaliGemmaForConditionalGeneration(config=config.paligemma_config) + self.gemma_expert = GemmaForCausalLM(config=config.gemma_expert_config) + # Remove unused embed_tokens + self.gemma_expert.model.embed_tokens = None + + self.to_bfloat16_like_physical_intelligence() + self.set_requires_grad() + + def set_requires_grad(self): + if self.config.freeze_vision_encoder: + self.paligemma.vision_tower.eval() + for params in self.paligemma.vision_tower.parameters(): + params.requires_grad = False + + if self.config.train_expert_only: + self.paligemma.eval() + for params in self.paligemma.parameters(): + params.requires_grad = False + + def train(self, mode: bool = True): + super().train(mode) + + if self.config.freeze_vision_encoder: + self.paligemma.vision_tower.eval() + + if self.config.train_expert_only: + self.paligemma.eval() + + def to_bfloat16_like_physical_intelligence(self): + self.paligemma = self.paligemma.to(dtype=torch.bfloat16) + + params_to_change_dtype = [ + "language_model.model.layers", + "gemma_expert.model.layers", + "vision_tower", + "multi_modal", + ] + for name, param in self.named_parameters(): + if any(selector in name for selector in params_to_change_dtype): + param.data = param.data.to(dtype=torch.bfloat16) + + def embed_image(self, image: torch.Tensor): + # Handle different transformers versions + if hasattr(self.paligemma, "get_image_features"): + return self.paligemma.get_image_features(image) + else: + return self.paligemma.model.get_image_features(image) + + def embed_language_tokens(self, tokens: torch.Tensor): + return self.paligemma.language_model.model.embed_tokens(tokens) + + # TODO: break down this huge forward into modules or functions + def forward( + self, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None, + inputs_embeds: List[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + fill_kv_cache: Optional[bool] = None, + ): + models = [self.paligemma.language_model.model, self.gemma_expert.model] + + for hidden_states in inputs_embeds: + # TODO this is very inefficient + # dtype is always the same, batch size too (if > 1 len) + # device could be trickier in multi gpu edge cases but that's it + if hidden_states is None: + continue + batch_size = hidden_states.shape[0] + + # RMSNorm + num_layers = self.paligemma.config.text_config.num_hidden_layers + head_dim = self.paligemma.config.text_config.head_dim + for layer_idx in range(num_layers): + query_states = [] + key_states = [] + value_states = [] + for i, hidden_states in enumerate(inputs_embeds): + if hidden_states is None: + continue + layer = models[i].layers[layer_idx] + # normalizer = torch.tensor(models[i].config.hidden_size**0.5, dtype=hidden_states.dtype) + # hidden_states = hidden_states * normalizer + hidden_states = layer.input_layernorm(hidden_states) + + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, layer.self_attn.head_dim) + + hidden_states = hidden_states.to(dtype=torch.bfloat16) + query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape) + key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape) + value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape) + + query_states.append(query_state) + key_states.append(key_state) + value_states.append(value_state) + + # B,L,H,D with L sequence length, H number of heads, D head dim + # concatenate on the number of embeddings/tokens + query_states = torch.cat(query_states, dim=1) + key_states = torch.cat(key_states, dim=1) + value_states = torch.cat(value_states, dim=1) + + query_states = apply_rope(query_states, position_ids) + key_states = apply_rope(key_states, position_ids) + + if use_cache and past_key_values is None: + past_key_values = {} + + if use_cache: + if fill_kv_cache: + past_key_values[layer_idx] = { + "key_states": key_states, + "value_states": value_states, + } + else: + # TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before. + # so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach + # the max len, then we (for instance) double the cache size. This implementation already exists + # in `transformers`. (molbap) + key_states = torch.cat([past_key_values[layer_idx]["key_states"], key_states], dim=1) + value_states = torch.cat( + [past_key_values[layer_idx]["value_states"], value_states], dim=1 + ) + + attention_interface = self.get_attention_interface() + att_output = attention_interface( + attention_mask, batch_size, head_dim, query_states, key_states, value_states + ) + att_output = att_output.to(dtype=torch.bfloat16) + + # first part of att_output is prefix (up to sequence length, [:, 0:prefix_seq_len]) + outputs_embeds = [] + start = 0 + for i, hidden_states in enumerate(inputs_embeds): + layer = models[i].layers[layer_idx] + + if hidden_states is not None: + end = start + hidden_states.shape[1] + + if att_output.dtype != layer.self_attn.o_proj.weight.dtype: + att_output = att_output.to(layer.self_attn.o_proj.weight.dtype) + out_emb = layer.self_attn.o_proj(att_output[:, start:end]) + + # TODO: first dropout (by default 0.0) + + # first residual + out_emb += hidden_states + after_first_residual = out_emb.clone() + + out_emb = layer.post_attention_layernorm(out_emb) + out_emb = layer.mlp(out_emb) + + # TODO: second dropout (by default 0.0) + + # second residual + out_emb += after_first_residual + + outputs_embeds.append(out_emb) + + start = end + else: + outputs_embeds.append(None) + + inputs_embeds = outputs_embeds + + # final norm + outputs_embeds = [] + for i, hidden_states in enumerate(inputs_embeds): + if hidden_states is not None: + out_emb = models[i].norm(hidden_states) + outputs_embeds.append(out_emb) + else: + outputs_embeds.append(None) + + return outputs_embeds, past_key_values + + def get_attention_interface(self): + if self.config.attention_implementation == "fa2": + attention_interface = self.flash_attention_forward + elif self.config.attention_implementation == "flex": + attention_interface = flex_attention_forward + else: + attention_interface = self.eager_attention_forward + return attention_interface + + def flash_attention_forward( + self, attention_mask, batch_size, head_dim, query_states, key_states, value_states + ): + raise NotImplementedError("FA2 is not implemented (yet)") + + def eager_attention_forward( + self, attention_mask, batch_size, head_dim, query_states, key_states, value_states + ): + num_att_heads = self.config.paligemma_config.text_config.num_attention_heads + num_key_value_heads = self.config.paligemma_config.text_config.num_key_value_heads + num_key_value_groups = num_att_heads // num_key_value_heads + + # query_states: batch_size, sequence_length, num_att_head, head_dim + # key_states: batch_size, sequence_length, num_key_value_head, head_dim + # value_states: batch_size, sequence_length, num_key_value_head, head_dim + sequence_length = key_states.shape[1] + + key_states = key_states[:, :, :, None, :].expand( + batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim + ) + key_states = key_states.reshape( + batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim + ) + + value_states = value_states[:, :, :, None, :].expand( + batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim + ) + value_states = value_states.reshape( + batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim + ) + + # Attention here is upcasted to float32 to match the original eager implementation. + + query_states = query_states.to(dtype=torch.float32) + key_states = key_states.to(dtype=torch.float32) + + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + + att_weights = torch.matmul(query_states, key_states.transpose(2, 3)) + att_weights *= head_dim**-0.5 + big_neg = -2.3819763e38 # See gemma/modules.py + + masked_att_weights = torch.where(attention_mask[:, None, :, :], att_weights, big_neg) + + probs = nn.functional.softmax(masked_att_weights, dim=-1) + probs = probs.to(dtype=value_states.dtype) + + # probs: batch_size, num_key_value_head, num_att_head, sequence_length, sequence_length + # value_states: batch_size, sequence_length, num_att_heads, head_dim + + att_output = torch.matmul(probs, value_states.permute(0, 2, 1, 3)) + + att_output = att_output.permute(0, 2, 1, 3) + # we use -1 because sequence length can change + att_output = att_output.reshape(batch_size, -1, num_key_value_heads * num_key_value_groups * head_dim) + + return att_output diff --git a/lerobot/common/policies/pi0fast/configuration_pi0fast.py b/lerobot/common/policies/pi0fast/configuration_pi0fast.py new file mode 100644 index 0000000000000000000000000000000000000000..137951792905c0efed669f37e74bc2e67ae15ed7 --- /dev/null +++ b/lerobot/common/policies/pi0fast/configuration_pi0fast.py @@ -0,0 +1,136 @@ +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamWConfig +from lerobot.common.optim.schedulers import ( + CosineDecayWithWarmupSchedulerConfig, +) +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature + + +@PreTrainedConfig.register_subclass("pi0fast") +@dataclass +class PI0FASTConfig(PreTrainedConfig): + # Input / output structure. + n_obs_steps: int = 1 + chunk_size: int = 10 + n_action_steps: int = 5 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.IDENTITY, + "STATE": NormalizationMode.MEAN_STD, + "ACTION": NormalizationMode.MEAN_STD, + } + ) + + # Shorter state and action vectors will be padded + max_state_dim: int = 32 # 32 + max_action_dim: int = 32 # 32 + + # Image preprocessing + resize_imgs_with_padding: tuple[int, int] = (224, 224) + interpolate_like_pi: bool = False + + # Add empty images. Used by pi0_aloha_sim which adds the empty + # left and right wrist cameras in addition to the top camera. + empty_cameras: int = 0 + + # Converts the joint and gripper values from the standard Aloha space to + # the space used by the pi internal runtime which was used to train the base model. + adapt_to_pi_aloha: bool = False + + # Converts joint dimensions to deltas with respect to the current state before passing to the model. + # Gripper dimensions will remain in absolute values. + use_delta_joint_actions_aloha: bool = False + + # Tokenizer + tokenizer_max_length: int = 48 + + # Projector + proj_width: int = 1024 + + # Decoding + max_decoding_steps: int = 256 + fast_skip_tokens: int = 128 # Skip last 128 tokens in PaliGemma vocab since they are special tokens + max_input_seq_len: int = 256 # 512 + + # Utils + use_cache: bool = True + + # Frozen parameters + freeze_vision_encoder: bool = True + freeze_lm_head: bool = True + + # Training presets + optimizer_lr: float = 1e-4 + optimizer_betas: tuple[float, float] = (0.9, 0.95) + optimizer_eps: float = 1e-8 + optimizer_weight_decay: float = 1e-5 + + scheduler_warmup_steps: int = 1_000 + scheduler_decay_steps: int = 30_000 + scheduler_decay_lr: float = 2.5e-6 + + checkpoint_path: str = None + + padding_side: str = "right" + + precision: str = "bfloat16" + grad_clip_norm: float = 1 + + # Allows padding/truncation of generated action tokens during detokenization to ensure decoding. + # In the original version, tensors of 0s were generated if shapes didn't match for stable decoding. + relaxed_action_decoding: bool = True + + def __post_init__(self): + super().__post_init__() + + """Input validation (not exhaustive).""" + if self.n_action_steps > self.chunk_size: + raise ValueError( + f"The chunk size is the upper bound for the number of action steps per model invocation. Got " + f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`." + ) + if self.n_obs_steps != 1: + raise ValueError( + f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`" + ) + + def validate_features(self) -> None: + for i in range(self.empty_cameras): + key = f"observation.images.empty_camera_{i}" + empty_camera = PolicyFeature( + type=FeatureType.VISUAL, + shape=(3, 480, 640), + ) + self.input_features[key] = empty_camera + + def get_optimizer_preset(self) -> AdamWConfig: + return AdamWConfig( + lr=self.optimizer_lr, + betas=self.optimizer_betas, + eps=self.optimizer_eps, + weight_decay=self.optimizer_weight_decay, + grad_clip_norm=self.grad_clip_norm, + ) + + def get_scheduler_preset(self): + return CosineDecayWithWarmupSchedulerConfig( + peak_lr=self.optimizer_lr, + decay_lr=self.scheduler_decay_lr, + num_warmup_steps=self.scheduler_warmup_steps, + num_decay_steps=self.scheduler_decay_steps, + ) + + @property + def observation_delta_indices(self) -> None: + return None + + @property + def action_delta_indices(self) -> list: + return list(range(self.chunk_size)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/pi0fast/modeling_pi0fast.py b/lerobot/common/policies/pi0fast/modeling_pi0fast.py new file mode 100644 index 0000000000000000000000000000000000000000..89cc5c2df608e095c483fabe0f89a98d2ed33bda --- /dev/null +++ b/lerobot/common/policies/pi0fast/modeling_pi0fast.py @@ -0,0 +1,977 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +π0+FAST: Efficient Action Tokenization for Vision-Language-Action Models + +[Paper](https://huggingface.co/papers/2501.09747) +[Jax code](https://github.com/Physical-Intelligence/openpi) + +Designed by Physical Intelligence. Ported from Jax by Hugging Face. + +Example of finetuning the pi0+FAST pretrained model (`pi0_fast_base` in `openpi`): +```bash +python lerobot/scripts/train.py \ +--policy.path=lerobot/pi0fast_base \ +--dataset.repo_id=danaaubakirova/koch_test +``` + +Example of training the pi0+FAST neural network with from scratch: +```bash +python lerobot/scripts/train.py \ +--policy.type=pi0fast \ +--dataset.repo_id=danaaubakirova/koch_test +``` + +Example of using the pi0 pretrained model outside LeRobot training framework: +```python +policy = PI0FASTPolicy.from_pretrained("lerobot/pi0fast_base") +``` + +""" + +from collections import deque +from functools import partial + +import numpy as np +import torch +import torch.nn.functional as F # noqa: N812 +from PIL import Image +from scipy.fft import idct +from torch import Tensor, nn +from transformers import AutoProcessor, AutoTokenizer, PaliGemmaForConditionalGeneration +from transformers.cache_utils import HybridCache, StaticCache +from transformers.models.auto import CONFIG_MAPPING + +from lerobot.common.constants import ACTION, OBS_STATE +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig +from lerobot.common.policies.pretrained import PreTrainedPolicy + +PRECISION = { + "float16": torch.float16, + "float32": torch.float32, + "bfloat16": torch.bfloat16, +} + + +def normalize(x, min_val, max_val): + return (x - min_val) / (max_val - min_val) + + +def unnormalize(x, min_val, max_val): + return x * (max_val - min_val) + min_val + + +def safe_arcsin(value): + # This ensures that the input stays within + # [−1,1] to avoid invalid values for arcsin + return torch.arcsin(torch.clamp(value, -1.0, 1.0)) + + +def aloha_gripper_to_angular(value): + # Aloha transforms the gripper positions into a linear space. The following code + # reverses this transformation to be consistent with pi0 which is pretrained in + # angular space. + # + # These values are coming from the Aloha code: + # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED + value = unnormalize(value, min_val=0.01844, max_val=0.05800) + + # This is the inverse of the angular to linear transformation inside the Interbotix code. + def linear_to_radian(linear_position, arm_length, horn_radius): + value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position) + return safe_arcsin(value) + + # The constants are taken from the Interbotix code. + value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022) + + # Normalize to [0, 1]. + # The values 0.4 and 1.5 were measured on an actual Trossen robot. + return normalize(value, min_val=0.4, max_val=1.5) + + +def aloha_gripper_from_angular(value): + # Convert from the gripper position used by pi0 to the gripper position that is used by Aloha. + # Note that the units are still angular but the range is different. + + # The values 0.4 and 1.5 were measured on an actual Trossen robot. + value = unnormalize(value, min_val=0.4, max_val=1.5) + + # These values are coming from the Aloha code: + # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE + return normalize(value, min_val=-0.6213, max_val=1.4910) + + +def aloha_gripper_from_angular_inv(value): + # Directly inverts the gripper_from_angular function. + value = unnormalize(value, min_val=-0.6213, max_val=1.4910) + return normalize(value, min_val=0.4, max_val=1.5) + + +class PI0FASTPolicy(PreTrainedPolicy): + """Wrapper class around PI0FAST tokenizer and model to train and run inference within LeRobot.""" + + config_class = PI0FASTConfig + name = "pi0fast" + + def __init__( + self, + config: PI0FASTConfig, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + + super().__init__(config) + config.validate_features() + self.config = config + + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + self.language_tokenizer = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224") + self.model = PI0FAST(config) + + self.reset() + + def reset(self): + """This should be called whenever the environment is reset.""" + self._action_queue = deque([], maxlen=self.config.n_action_steps) + + def get_optim_params(self) -> dict: + return self.parameters() + + def _pi_aloha_decode_state(self, state): + # Flip the joints. + for motor_idx in [1, 2, 8, 9]: + state[:, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx]) + return state + + def _pi_aloha_encode_actions(self, actions): + # Flip the joints. + for motor_idx in [1, 2, 8, 9]: + actions[:, :, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx]) + return actions + + def _pi_aloha_encode_actions_inv(self, actions): + # Flip the joints again. + for motor_idx in [1, 2, 8, 9]: + actions[:, :, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx]) + return actions + + @torch.no_grad + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations. + + This method wraps `select_actions` in order to return one action at a time for execution in the + environment. It works by managing the actions in a queue and only calling `select_actions` when the + queue is empty. + """ + self.eval() + + if self.config.adapt_to_pi_aloha: + batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + + batch = self.normalize_inputs(batch) + + # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by + # querying the policy. + if len(self._action_queue) == 0: + actions = self.model.generate_actions(batch) + + actions = actions[:, : self.config.n_action_steps] + + original_action_dim = self.config.action_feature.shape[ + 0 + ] # self.config.max_action_dim # self.config.action_feature.shape[0] + actions = actions[:, :, :original_action_dim] + + actions = self.unnormalize_outputs({"action": actions})["action"] + + if self.config.adapt_to_pi_aloha: + actions = self._pi_aloha_encode_actions(actions) + + # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue + # effectively has shape (n_action_steps, batch_size, *), hence the transpose. + self._action_queue.extend(actions.transpose(0, 1)) + return self._action_queue.popleft() + + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + if self.config.adapt_to_pi_aloha: + batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION]) + batch = self.normalize_inputs(batch) + batch = self.normalize_targets(batch) + loss_dict = self.model.forward(batch) + return loss_dict["loss"], loss_dict + + +def block_causal_update_causal_mask( + attention_mask, + token_type_ids=None, + past_key_values=None, + cache_position=None, + input_tensor=None, + attn_implementation: str = "eager", + dtype: torch.dtype = "float32", +): + """ + Update the causal mask during training and generation. It can be customized to different attention masks. + """ + if attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + using_static_cache = isinstance(past_key_values, StaticCache) + min_dtype = torch.finfo(dtype).min + + if input_tensor is None: + input_tensor = attention_mask + + inputs_lead_dim, sequence_length = input_tensor.shape[:2] + + if using_static_cache or isinstance(past_key_values, HybridCache): + target_length = past_key_values.get_max_cache_shape() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else cache_position[0] + sequence_length + 1 + ) + + # Handle precomputed attention masks + if attention_mask is not None and attention_mask.dim() == 4: + return attention_mask + + # Causal mask initialization + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device + ) + + # Standard causal masking (triu ensures tokens can only attend to past) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + + # Apply block causal mask + if token_type_ids is not None: + token_type_ids = token_type_ids.to(causal_mask.device).bool() + cumsum = torch.cumsum(token_type_ids, dim=1) + block_causal_mask = cumsum[:, None, :] <= cumsum[:, :, None] + + # Combine causal_mask with block-wise attention mask + causal_mask = torch.where(block_causal_mask, 0.0, causal_mask) + causal_mask = causal_mask[:, None, :, :] + else: + # Apply past cache position constraint + causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape( + -1, 1 + ) + causal_mask = causal_mask[None, None, :, :].expand(inputs_lead_dim, 1, -1, -1) + else: + # Apply past cache position constraint + causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape( + -1, 1 + ) + causal_mask = causal_mask[None, None, :, :].expand(inputs_lead_dim, 1, -1, -1) + + if attention_mask is not None: + causal_mask = causal_mask.clone() # Copy to contiguous memory for in-place edits + mask_length = attention_mask.shape[-1] + + # Apply padding mask + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( + causal_mask.device + ) + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + +def prepare_inputs_for_generation( + # self, + input_ids, + past_key_values=None, + inputs_embeds=None, + cache_position=None, + position_ids=None, + pixel_values=None, + attention_mask=None, + token_type_ids=None, + use_cache=True, + num_logits_to_keep=None, + labels=None, + self=None, + **kwargs, +): + # create block causal attention + if cache_position[0] > 0 and input_ids.shape[1] > 0: + input_tensor = input_ids[:, -1:] + new_positions = ( + torch.ones( + (position_ids.shape[0], input_ids.shape[1]), + dtype=position_ids.dtype, + device=position_ids.device, + ).cumsum(-1) + + position_ids[:, -1:] + ) + position_ids = torch.cat([position_ids, new_positions], dim=-1) + else: + input_tensor = inputs_embeds + attention_mask = block_causal_update_causal_mask( + attention_mask=attention_mask, + past_key_values=past_key_values, + cache_position=cache_position, + input_tensor=input_tensor, + token_type_ids=token_type_ids, + dtype=self.dtype, + attn_implementation=self.config.text_config._attn_implementation, + ) + # Overwritten -- custom `position_ids` and `pixel_values` handling + model_inputs = self.language_model.prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + cache_position=cache_position, + use_cache=use_cache, + num_logits_to_keep=num_logits_to_keep, + token_type_ids=token_type_ids, + **kwargs, + ) + + # Position_ids in Paligemma are 1-indexed + if model_inputs.get("position_ids") is not None: + model_inputs["position_ids"] += 1 + # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore + # Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always + if cache_position[0] == 0: + model_inputs["pixel_values"] = pixel_values + is_training = token_type_ids is not None and labels is not None + if cache_position[0] == 0 and isinstance(past_key_values, HybridCache): + input_tensor = inputs_embeds if inputs_embeds is not None else input_ids + causal_mask = self._update_causal_mask( + attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training + ) + model_inputs["attention_mask"] = causal_mask + + return model_inputs + + +class PI0FAST(nn.Module): + def __init__(self, config: PI0FASTConfig): + super().__init__() + self.config = config + + # TODO: move tokenizers in Policy + fast_tokenizer_path = "physical-intelligence/fast" + pi0_paligemma_path = "google/paligemma-3b-pt-224" + self.paligemma_tokenizer = AutoTokenizer.from_pretrained(pi0_paligemma_path) + self.processor = AutoProcessor.from_pretrained(pi0_paligemma_path) + self.fast_tokenizer = AutoProcessor.from_pretrained(fast_tokenizer_path, trust_remote_code=True) + self.fast_skip_tokens = self.config.fast_skip_tokens + self.max_input_seq_len = self.config.max_input_seq_len + self.action_horizon = self.config.chunk_size + self.action_dim = self.config.action_feature.shape[ + 0 + ] # self.config.max_action_dim # self.config.action_feature.shape[0] + precision = config.precision + torch_precision = PRECISION.get(precision, torch.float32) + self.pad_token_id = ( + self.paligemma_tokenizer.pad_token_id + if hasattr(self.paligemma_tokenizer, "pad_token_id") + else self.paligemma_tokenizer.eos_token_id + ) + + paligemma_config = CONFIG_MAPPING["paligemma"]( + transformers_version="4.48.1", + _vocab_size=257152, + bos_token_id=2, + eos_token_id=1, + hidden_size=2048, + image_token_index=257152, + model_type="paligemma", + pad_token_id=0, + projection_dim=2048, + text_config={ + "hidden_activation": "gelu_pytorch_tanh", + "hidden_size": 2048, + "intermediate_size": 16384, + "model_type": "gemma", + "num_attention_heads": 8, + "num_hidden_layers": 18, + "num_image_tokens": 256, + "num_key_value_heads": 1, + "torch_dtype": precision, + "vocab_size": 257152, + "_attn_implementation": "eager", + }, + vision_config={ + "hidden_size": 1152, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "num_image_tokens": 256, + "patch_size": 14, + "projection_dim": 2048, + "projector_hidden_act": "gelu_pytorch_tanh", + "torch_dtype": precision, + "vision_use_head": False, + }, + ) + self.pi0_paligemma = PaliGemmaForConditionalGeneration(config=paligemma_config) + + self.pi0_paligemma.prepare_inputs_for_generation = partial( + prepare_inputs_for_generation, self=self.pi0_paligemma + ) + # change important stuff in bf16 + params_to_change_dtype = [ + "language_model", + "vision_tower", + "multi_modal", + ] + for name, param in self.pi0_paligemma.named_parameters(): + if any(selector in name for selector in params_to_change_dtype): + param.data = param.data.to(dtype=torch_precision) + self.set_requires_grad() + self.image_keys = self.config.image_features.keys() + self.ignore_index = self.pi0_paligemma.config.ignore_index + self.padding_side = self.config.padding_side + + def set_requires_grad(self): + if self.config.freeze_vision_encoder: + self.pi0_paligemma.vision_tower.eval() + for params in self.pi0_paligemma.vision_tower.parameters(): + params.requires_grad = False + # To avoid unused params issue with distributed training + if self.config.freeze_lm_head: + for name, params in self.pi0_paligemma.named_parameters(): + if "embed_tokens" in name: # lm heads and embedding layer are tied + params.requires_grad = False + + def embed_tokens(self, tokens: torch.Tensor): + return self.pi0_paligemma.language_model.model.embed_tokens(tokens) + + def prepare_inputs_for_generation(self, *args, **kwargs): + return self.pi0_paligemma.prepare_inputs_for_generation(*args, **kwargs) + + def prepare_images(self, batch): + """Preprocess LeRobot batch into Pi0 inputs""" + images = [] + img_masks = [] + present_img_keys = [key for key in self.image_keys if key in batch] + if len(present_img_keys) == 0: + raise ValueError( + f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})" + ) + + # Preprocess image features present in the batch + num_empty_cameras = 0 + for key in self.image_keys: + if key in present_img_keys: + img = batch[key] + + if self.config.resize_imgs_with_padding is not None: + img = resize_with_pad( + img, + *self.config.resize_imgs_with_padding, + pad_value=0, + interpolate_like_pi=self.config.interpolate_like_pi, + ) + + # Normalize from range [0,1] to [-1,1] as expected by siglip + img = img * 2.0 - 1.0 + + bsize = img.shape[0] + device = img.device + mask = torch.ones(bsize, dtype=torch.bool, device=device) + else: + if num_empty_cameras >= self.config.empty_cameras: + continue + img = torch.ones_like(img) * -1 + bsize = img.shape[0] + device = img.device + mask = torch.ones(bsize, dtype=torch.bool, device=device) + num_empty_cameras += 1 + + images.append(img) + img_masks.append(mask) + return images, img_masks + + def normalize_actions(self, actions: torch.Tensor) -> torch.Tensor: + mins = actions.amin(dim=(1, 2), keepdim=True) # [0] + maxs = actions.amax(dim=(1, 2), keepdim=True) # [0] + return 2 * (actions - mins) / (maxs - mins + 1e-8) - 1 + + def _act_tokens_to_paligemma_tokens(self, tokens: torch.Tensor) -> torch.Tensor: + out = self.paligemma_tokenizer.vocab_size - 1 - self.fast_skip_tokens - tokens + return out + + def fast_tokenizer_wrapper(self, actions_norm): + """ + A wrapper for self.fast_tokenizer that ensures batch processing, + conversion to PyTorch tensors, and returns a dictionary without padding. + """ + batch_tokens = self.fast_tokenizer(actions_norm) + fast_out = self.processor.tokenizer.pad({"input_ids": batch_tokens}, return_tensors="pt") + + return fast_out + + def create_token_type_ids(self, padded_mask: torch.Tensor, prefix_len: int) -> torch.Tensor: + token_type_ids = torch.zeros_like(padded_mask, dtype=torch.bool) + # Compute cumulative sum mask + cumsum_mask = (padded_mask != 0).cumsum(dim=1) + # Suffix block (everything after prefix_len) + suffix_mask = cumsum_mask > prefix_len + token_type_ids = suffix_mask + return token_type_ids + + def create_input_tokens(self, state, lang_text, actions=None): + bsize = state.shape[0] + device = state.device + bins = torch.linspace(-1, 1, 256 + 1, device=device)[:-1] + discretized = torch.bucketize(state, bins) - 1 + discretized = discretized[:, :32] + + prefix_texts = [] + state_text = [] + for txt, disc in zip(lang_text, discretized, strict=False): + cleaned = txt.lower().strip().replace("_", " ") + state_str = " ".join(str(val.item()) for val in disc) + prefix_texts.append(f"Task: {cleaned}, State: {state_str};\n") + state_text.append(f"State: {state_str};\n") + + prefix_out = self.paligemma_tokenizer( + prefix_texts, add_special_tokens=True, return_tensors="pt", padding="longest", truncation=False + ) + prefix_ids = prefix_out["input_ids"].to(device) + prefix_mask = prefix_out["attention_mask"].to(device) + prefix_lens = prefix_mask.sum(dim=1)[:, None].cpu() + + if actions is not None: + actions_norm = self.normalize_actions(actions) + actions_pad = F.pad( + actions_norm, (0, max(0, self.config.max_action_dim - actions_norm.shape[2])), value=0 + )[:, :, : self.config.max_action_dim] + fast_out = self.fast_tokenizer_wrapper( + actions_pad.cpu(), + ) + act_ids = fast_out["input_ids"] + act_mask = fast_out["attention_mask"].to(device) + + act_ids = self._act_tokens_to_paligemma_tokens(act_ids).to(device) + # Replace action with 0 to pad tokens + act_ids = torch.where( + act_ids == self.paligemma_tokenizer.vocab_size - 1 - self.fast_skip_tokens, + self.pad_token_id, + act_ids, + ) + + eos_token = torch.tensor( + [self.paligemma_tokenizer.eos_token_id], dtype=torch.long, device=device + ).expand(bsize, -1) + eos_mask = torch.tensor([1], dtype=torch.long, device=device).expand(bsize, -1) + bos = self.paligemma_tokenizer("Action: ", add_special_tokens=False, return_tensors="pt") + bos_token = bos["input_ids"].expand(act_ids.shape[0], -1).to(device) + bos_mask = bos["attention_mask"].expand(act_ids.shape[0], -1).to(device) + act_ids = torch.cat([bos_token, act_ids, eos_token], dim=1) + act_mask = torch.cat([bos_mask, act_mask, eos_mask], dim=1) + act_mask = act_mask.to(device) + else: + act_ids = torch.empty(bsize, self.pad_token_id, dtype=torch.long, device=device) + act_mask = torch.empty(bsize, 0, dtype=torch.long, device=device) + final_ids = torch.cat([prefix_ids, act_ids], dim=1) + + final_mask = torch.cat([prefix_mask, act_mask], dim=1) + batch_inputs = {"input_ids": final_ids.tolist(), "attention_mask": final_mask.tolist()} + + # Use tokenizer pad function + padded_output = self.paligemma_tokenizer.pad( + batch_inputs, padding="longest", max_length=180, return_tensors="pt" + ) + padded_mask = padded_output["attention_mask"] + + # define tensor of padding lengths + att_mask = (padded_mask != 0).cumsum(dim=1) > prefix_lens + + token_type_ids = self.create_token_type_ids(padded_mask=padded_mask, prefix_len=prefix_lens) + + padded_output["padded_mask"] = padded_output.pop("attention_mask") + padded_output["attention_mask"] = att_mask + # loss is computed not on prefix, and not on padding + padded_output["loss_mask"] = att_mask & padded_output["padded_mask"] + padded_output["token_type_ids"] = token_type_ids + return padded_output + + def shift_padding_side( + self, + tokens: torch.Tensor, + ar_mask: torch.Tensor, + padding_mask: torch.Tensor, + loss_mask: torch.Tensor, + targets: torch.Tensor, + token_type_ids: torch.Tensor, + padding_side: str = "right", + ) -> tuple[torch.Tensor]: + if padding_side not in ["right", "left"]: + return tokens, ar_mask, padding_mask, loss_mask, targets, token_type_ids + + new_tokens = torch.empty_like(tokens) + new_ar_masks = torch.empty_like(ar_mask) + new_padding_mask = torch.empty_like(padding_mask) + new_loss_mask = torch.empty_like(loss_mask) + new_targets = torch.empty_like(targets) + new_token_type_ids = torch.empty_like(token_type_ids) + batch_size = tokens.shape[0] + for i in range(batch_size): + padding_indices = torch.where(padding_mask[i] == 0)[0] + non_padding_indices = torch.where(padding_mask[i] == 1)[0] + if padding_side == "left": + new_indices = torch.cat((padding_indices, non_padding_indices), dim=0) + else: + new_indices = torch.cat((non_padding_indices, padding_indices), dim=0) + new_tokens[i] = tokens[i].index_select(0, new_indices) + new_ar_masks[i] = ar_mask[i].index_select(0, new_indices) + new_padding_mask[i] = padding_mask[i].index_select(0, new_indices) + new_loss_mask[i] = loss_mask[i].index_select(0, new_indices) + new_targets[i] = targets[i].index_select(0, new_indices) + new_token_type_ids[i] = token_type_ids[i].index_select(0, new_indices) + + return new_tokens, new_ar_masks, new_padding_mask, new_loss_mask, new_targets, new_token_type_ids + + def forward(self, batch: dict[str, Tensor]): + device = batch[OBS_STATE].device + # TODO: keep like this or move to the policy .forward + images, img_masks = self.prepare_images(batch) + + padded_outs = self.create_input_tokens( + state=batch[OBS_STATE], + lang_text=batch["task"], + actions=batch[ACTION], + ) + + embs, pad_masks, _, targets, loss_mask, token_type_ids = self.embed_inputs( + images, + img_masks, + padded_outs["input_ids"], + padded_outs["padded_mask"], + padded_outs["attention_mask"], + padded_outs["loss_mask"], + padded_outs["token_type_ids"], + padding_side=self.padding_side, + ) + position_ids = torch.cumsum(pad_masks, dim=1) - 1 + token_type_ids = token_type_ids.to(dtype=torch.int64) + past_seen_tokens = 0 + cache_position = torch.arange(past_seen_tokens, past_seen_tokens + embs.shape[1], device=embs.device) + pad_masks = block_causal_update_causal_mask( + attention_mask=pad_masks, + past_key_values=None, + cache_position=cache_position, + input_tensor=embs, + token_type_ids=token_type_ids, + dtype=self.pi0_paligemma.dtype, + attn_implementation=self.pi0_paligemma.config.text_config._attn_implementation, + ) + outputs = self.pi0_paligemma.forward( + input_ids=None, + token_type_ids=None, + attention_mask=pad_masks, + position_ids=position_ids, + past_key_values=None, + inputs_embeds=embs, + use_cache=False, + labels=None, + ) + + logits = outputs.logits + + loss_fct = nn.CrossEntropyLoss(reduction="none") + + # Shift left for next-step prediction + logits = logits[:, :-1, :] + targets = targets[:, 1:].to(device) # Shift targets + loss_mask = loss_mask[:, 1:].to(device) # Ensure correct shape + + # Compute per-token loss + token_loss = loss_fct(logits.reshape(-1, logits.shape[-1]), targets.reshape(-1)) + + # Apply loss mask + token_loss = token_loss * loss_mask.reshape(-1) + + # Compute final loss + loss = token_loss.sum() / torch.clamp(loss_mask.sum(), min=1) + + # Return loss dictionary + loss_dict = {"ce_loss": loss.item(), "loss": loss} + return loss_dict + + def decode_actions_with_fast( + self, + tokens: list[list[int]], + *, + time_horizon: int | None = None, + action_dim: int | None = None, + relaxed_decoding: bool = True, + ) -> np.array: + """ + Adapt original decoding in FAST to always return actions instead of zeros. + """ + self.time_horizon = ( + time_horizon or self.fast_tokenizer.time_horizon or self.fast_tokenizer.called_time_horizon + ) + self.action_dim = ( + action_dim or self.fast_tokenizer.action_dim or self.fast_tokenizer.called_action_dim + ) + + # Cache the time horizon and action dimension for the next call + self.called_time_horizon = self.time_horizon + self.called_action_dim = self.action_dim + + assert self.time_horizon is not None and self.action_dim is not None, ( + "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim." + ) + + decoded_actions = [] + for token in tokens: + try: + decoded_tokens = self.fast_tokenizer.bpe_tokenizer.decode(token) + decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.fast_tokenizer.min_token + if relaxed_decoding: + # Expected sequence length + expected_seq_len = self.time_horizon * self.action_dim + diff = expected_seq_len - decoded_dct_coeff.shape[0] + # Apply truncation if too long + if diff < 0: + decoded_dct_coeff = decoded_dct_coeff[:expected_seq_len] # Truncate on the right + # Apply padding if too short + elif diff > 0: + decoded_dct_coeff = np.pad( + decoded_dct_coeff, (0, diff), mode="constant", constant_values=0 + ) + + decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim) + assert decoded_dct_coeff.shape == ( + self.time_horizon, + self.action_dim, + ), ( + f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})" + ) + except Exception as e: + print(f"Error decoding tokens: {e}") + print(f"Tokens: {token}") + decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim)) + decoded_actions.append(idct(decoded_dct_coeff / self.fast_tokenizer.scale, axis=0, norm="ortho")) + return np.stack(decoded_actions) + + def extract_actions(self, tokens: torch.Tensor, action_horizon: int, action_dim: int) -> torch.Tensor: + """ + Extracts actions from predicted output tokens using the FAST model. + + Args: + tokens (torch.Tensor): The input tensor of tokenized outputs. + action_horizon (int): The number of timesteps for actions. + action_dim (int): The dimensionality of each action. + + Returns: + torch.Tensor: The extracted actions as a tensor of shape (action_horizon, action_dim). + """ + # Decode predicted output tokens + decoded_tokens = self.paligemma_tokenizer.batch_decode(tokens, skip_special_tokens=True) + cleaned_tokens = [ + tokens_sequence.replace("Action:", "").replace(":", "").strip().split("|")[0].strip() + for tokens_sequence in decoded_tokens + ] + raw_action_tokens = [ + self.processor.tokenizer.encode(sample_tokens, return_tensors="pt", padding=False) + for sample_tokens in cleaned_tokens + ] # something like this should be robust #looks good + action_tokens = [ + self._act_tokens_to_paligemma_tokens(raw_action_token) for raw_action_token in raw_action_tokens + ] + # returns the tensor of decoded actions per sample in a list + decoded_actions = [ + torch.tensor( + self.decode_actions_with_fast( + tok.tolist(), + time_horizon=action_horizon, + action_dim=action_dim, + relaxed_decoding=self.config.relaxed_action_decoding, + ), + device=tokens.device, + ).squeeze(0) + for tok in action_tokens + ] + + return torch.stack( + decoded_actions, + dim=0, + ) + + def generate_actions(self, batch: dict[str, Tensor]): + # TODO: keep like this or move to the policy .forward + images, img_masks = self.prepare_images(batch) + + padded_outs = self.create_input_tokens(state=batch[OBS_STATE], lang_text=batch["task"], actions=None) + embs, pad_masks, att_masks2, targets, loss_mask, token_type_ids = self.embed_inputs( + images, + img_masks, + padded_outs["input_ids"], + padded_outs["padded_mask"], + padded_outs["attention_mask"], + padded_outs["loss_mask"], + padded_outs["token_type_ids"], + padding_side="left", + ) + token_type_ids = token_type_ids.to(dtype=torch.int64) + prefix_position_ids = torch.cumsum(pad_masks, dim=1) - 1 + output_tokens = self.pi0_paligemma.generate( + input_ids=None, + attention_mask=pad_masks, + position_ids=prefix_position_ids, + past_key_values=None, + inputs_embeds=embs, + use_cache=self.config.use_cache, + max_new_tokens=self.config.max_decoding_steps, + do_sample=False, + num_beams=1, + token_type_ids=token_type_ids, + ) + actions = self.extract_actions(output_tokens, self.action_horizon, self.action_dim) + return actions + + def embed_image(self, image: torch.Tensor): + # Handle different transformers versions + if hasattr(self.pi0_paligemma, "get_image_features"): + return self.pi0_paligemma.get_image_features(image) + else: + return self.pi0_paligemma.model.get_image_features(image) + + def embed_inputs( + self, + images, + img_masks, + tokens, + pad_mask, + ar_mask, + loss_mask, + token_type_ids, + padding_side: str = "right", + ): + # TODO: avoid list in python and torch.cat ; prefer pre-allocation with torch.empty + # images are a list of same size + # vectorizing everything! + device = images[0].device + image_embedding_dim = images[0].shape[-1] # TODO should be from self.config + all_images = torch.stack(images, dim=1).to(device) + b, n, c, h, w = all_images.shape + all_images = all_images.view(b * n, c, h, w) + embedded = self.embed_image(all_images).to(device) + b_n, p, image_embedding_dim = embedded.shape # Extract current dimensions + m = b_n // b # Compute the number of images per sample dynamically + + # Reshape dynamically + embedded = embedded.view(b, m, p, image_embedding_dim) + tokens_embs = self.embed_tokens(tokens.to(device)) + + img_masks = torch.stack(img_masks, dim=1).unsqueeze(-1).to(device) + num_img_emb = embedded.shape[2] + img_pad_masks = img_masks.repeat(1, 1, num_img_emb).view(b, -1) + img_att_masks = torch.zeros((b, n, num_img_emb), dtype=torch.long, device=device).reshape(b, -1) + + image_target_tokens = ( + torch.ones((b, n, num_img_emb), dtype=torch.long, device=device) * self.pad_token_id + ).reshape(b, -1) + image_loss_mask = torch.zeros((b, n, num_img_emb), dtype=torch.long, device=device).reshape(b, -1) + + embedded = embedded.reshape(b, n * num_img_emb, image_embedding_dim) # Shape: (B, N*P, D) + + embs = torch.cat([embedded, tokens_embs], dim=1).to(device) + pad_masks = torch.cat([img_pad_masks, pad_mask.to(device)], dim=1) + att_masks = torch.cat([img_att_masks, ar_mask.to(device)], dim=1) + loss_masks = torch.cat([image_loss_mask, loss_mask.to(device)], dim=1) + targets = torch.cat([image_target_tokens, tokens.to(device)], dim=1) + token_type_ids = torch.cat([img_att_masks, token_type_ids.to(device)], dim=1) + + # Shift pad tokens to the left (.generate()) or right (.train()) + embs, att_masks, pad_masks, loss_masks, targets, token_type_ids = self.shift_padding_side( + embs, att_masks, pad_masks, loss_masks, targets, token_type_ids, padding_side=padding_side + ) + + targets = torch.where(targets == self.pad_token_id, self.ignore_index, targets) + return embs, pad_masks, att_masks, targets, loss_masks, token_type_ids + + +def resize_with_pad(img, width, height, pad_value=0, interpolate_like_pi=True): + # assume no-op when width height fits already + if img.ndim != 4: + raise ValueError(f"(b,c,h,w) expected, but {img.shape}") + + cur_height, cur_width = img.shape[2:] + + ratio = max(cur_width / width, cur_height / height) + resized_height = int(cur_height / ratio) + resized_width = int(cur_width / ratio) + + if interpolate_like_pi: + img = (img * 255.0).to(dtype=torch.uint8) + img = img.permute(0, 2, 3, 1) + original_device = img.device + img = img.to(device="cpu").numpy() + imgs = [] + for sub_img in img: + sub_img = Image.fromarray(sub_img) + resized_img = sub_img.resize((resized_width, resized_height), resample=2) + resized_img = torch.from_numpy(np.array(resized_img)) + imgs.append(resized_img) + img = torch.stack(imgs, dim=0) + img = img.permute(0, 3, 1, 2) + resized_img = img.to(device=original_device, dtype=torch.float32) / 255.0 + else: + resized_img = F.interpolate( + img, size=(resized_height, resized_width), mode="bilinear", align_corners=False + ) + + pad_height = max(0, int(height - resized_height)) + pad_width = max(0, int(width - resized_width)) + + # pad on left and top of image + padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value) + return padded_img diff --git a/lerobot/common/policies/pretrained.py b/lerobot/common/policies/pretrained.py new file mode 100644 index 0000000000000000000000000000000000000000..bbd9c796676da4834319e4dc1a378b98a495c960 --- /dev/null +++ b/lerobot/common/policies/pretrained.py @@ -0,0 +1,199 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import abc +import logging +import os +from pathlib import Path +from typing import Type, TypeVar + +import packaging +import safetensors +from huggingface_hub import hf_hub_download +from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE +from huggingface_hub.errors import HfHubHTTPError +from safetensors.torch import load_model as load_model_as_safetensor +from safetensors.torch import save_model as save_model_as_safetensor +from torch import Tensor, nn + +from lerobot.common.utils.hub import HubMixin +from lerobot.configs.policies import PreTrainedConfig + +T = TypeVar("T", bound="PreTrainedPolicy") + +DEFAULT_POLICY_CARD = """ +--- +# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 +# Doc / guide: https://huggingface.co/docs/hub/model-cards +{{ card_data }} +--- + +This policy has been pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot): +- Docs: {{ docs_url | default("[More Information Needed]", true) }} +""" + + +class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC): + """ + Base class for policy models. + """ + + config_class: None + name: None + + def __init__(self, config: PreTrainedConfig, *inputs, **kwargs): + super().__init__() + if not isinstance(config, PreTrainedConfig): + raise ValueError( + f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class " + "`PreTrainedConfig`. To create a model from a pretrained model use " + f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`" + ) + self.config = config + + def __init_subclass__(cls, **kwargs): + super().__init_subclass__(**kwargs) + if not getattr(cls, "config_class", None): + raise TypeError(f"Class {cls.__name__} must define 'config_class'") + if not getattr(cls, "name", None): + raise TypeError(f"Class {cls.__name__} must define 'name'") + + def _save_pretrained(self, save_directory: Path) -> None: + self.config._save_pretrained(save_directory) + model_to_save = self.module if hasattr(self, "module") else self + save_model_as_safetensor(model_to_save, str(save_directory / SAFETENSORS_SINGLE_FILE)) + + @classmethod + def from_pretrained( + cls: Type[T], + pretrained_name_or_path: str | Path, + *, + config: PreTrainedConfig | None = None, + force_download: bool = False, + resume_download: bool | None = None, + proxies: dict | None = None, + token: str | bool | None = None, + cache_dir: str | Path | None = None, + local_files_only: bool = False, + revision: str | None = None, + strict: bool = False, + **kwargs, + ) -> T: + """ + The policy is set in evaluation mode by default using `policy.eval()` (dropout modules are + deactivated). To train it, you should first set it back in training mode with `policy.train()`. + """ + if config is None: + config = PreTrainedConfig.from_pretrained( + pretrained_name_or_path=pretrained_name_or_path, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + revision=revision, + **kwargs, + ) + model_id = str(pretrained_name_or_path) + instance = cls(config, **kwargs) + if os.path.isdir(model_id): + print("Loading weights from local directory") + model_file = os.path.join(model_id, SAFETENSORS_SINGLE_FILE) + policy = cls._load_as_safetensor(instance, model_file, config.device, strict) + else: + try: + model_file = hf_hub_download( + repo_id=model_id, + filename=SAFETENSORS_SINGLE_FILE, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + ) + policy = cls._load_as_safetensor(instance, model_file, config.device, strict) + except HfHubHTTPError as e: + raise FileNotFoundError( + f"{SAFETENSORS_SINGLE_FILE} not found on the HuggingFace Hub in {model_id}" + ) from e + + policy.to(config.device) + policy.eval() + return policy + + @classmethod + def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T: + if packaging.version.parse(safetensors.__version__) < packaging.version.parse("0.4.3"): + load_model_as_safetensor(model, model_file, strict=strict) + if map_location != "cpu": + logging.warning( + "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors." + " This means that the model is loaded on 'cpu' first and then copied to the device." + " This leads to a slower loading time." + " Please update safetensors to version 0.4.3 or above for improved performance." + ) + model.to(map_location) + else: + safetensors.torch.load_model(model, model_file, strict=strict, device=map_location) + return model + + # def generate_model_card(self, *args, **kwargs) -> ModelCard: + # card = ModelCard.from_template( + # card_data=self._hub_mixin_info.model_card_data, + # template_str=self._hub_mixin_info.model_card_template, + # repo_url=self._hub_mixin_info.repo_url, + # docs_url=self._hub_mixin_info.docs_url, + # **kwargs, + # ) + # return card + + @abc.abstractmethod + def get_optim_params(self) -> dict: + """ + Returns the policy-specific parameters dict to be passed on to the optimizer. + """ + raise NotImplementedError + + @abc.abstractmethod + def reset(self): + """To be called whenever the environment is reset. + + Does things like clearing caches. + """ + raise NotImplementedError + + # TODO(aliberts, rcadene): split into 'forward' and 'compute_loss'? + @abc.abstractmethod + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]: + """_summary_ + + Args: + batch (dict[str, Tensor]): _description_ + + Returns: + tuple[Tensor, dict | None]: The loss and potentially other information. Apart from the loss which + is a Tensor, all other items should be logging-friendly, native Python types. + """ + raise NotImplementedError + + @abc.abstractmethod + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Return one action to run in the environment (potentially in batch mode). + + When the model uses a history of observations, or outputs a sequence of actions, this method deals + with caching. + """ + raise NotImplementedError diff --git a/lerobot/common/policies/sac/configuration_sac.py b/lerobot/common/policies/sac/configuration_sac.py new file mode 100644 index 0000000000000000000000000000000000000000..3156e8c6e2ea27d1e6f6f98f5b8d7c447b3363b5 --- /dev/null +++ b/lerobot/common/policies/sac/configuration_sac.py @@ -0,0 +1,245 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.constants import ACTION, OBS_IMAGE, OBS_STATE +from lerobot.common.optim.optimizers import MultiAdamConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import NormalizationMode + + +def is_image_feature(key: str) -> bool: + """Check if a feature key represents an image feature. + + Args: + key: The feature key to check + + Returns: + True if the key represents an image feature, False otherwise + """ + return key.startswith(OBS_IMAGE) + + +@dataclass +class ConcurrencyConfig: + """Configuration for the concurrency of the actor and learner. + Possible values are: + - "threads": Use threads for the actor and learner. + - "processes": Use processes for the actor and learner. + """ + + actor: str = "threads" + learner: str = "threads" + + +@dataclass +class ActorLearnerConfig: + learner_host: str = "127.0.0.1" + learner_port: int = 50051 + policy_parameters_push_frequency: int = 4 + queue_get_timeout: float = 2 + + +@dataclass +class CriticNetworkConfig: + hidden_dims: list[int] = field(default_factory=lambda: [256, 256]) + activate_final: bool = True + final_activation: str | None = None + + +@dataclass +class ActorNetworkConfig: + hidden_dims: list[int] = field(default_factory=lambda: [256, 256]) + activate_final: bool = True + + +@dataclass +class PolicyConfig: + use_tanh_squash: bool = True + std_min: float = 1e-5 + std_max: float = 10.0 + init_final: float = 0.05 + + +@PreTrainedConfig.register_subclass("sac") +@dataclass +class SACConfig(PreTrainedConfig): + """Soft Actor-Critic (SAC) configuration. + + SAC is an off-policy actor-critic deep RL algorithm based on the maximum entropy + reinforcement learning framework. It learns a policy and a Q-function simultaneously + using experience collected from the environment. + + This configuration class contains all the parameters needed to define a SAC agent, + including network architectures, optimization settings, and algorithm-specific + hyperparameters. + """ + + # Mapping of feature types to normalization modes + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.MEAN_STD, + "STATE": NormalizationMode.MIN_MAX, + "ENV": NormalizationMode.MIN_MAX, + "ACTION": NormalizationMode.MIN_MAX, + } + ) + + # Statistics for normalizing different types of inputs + dataset_stats: dict[str, dict[str, list[float]]] | None = field( + default_factory=lambda: { + OBS_IMAGE: { + "mean": [0.485, 0.456, 0.406], + "std": [0.229, 0.224, 0.225], + }, + OBS_STATE: { + "min": [0.0, 0.0], + "max": [1.0, 1.0], + }, + ACTION: { + "min": [0.0, 0.0, 0.0], + "max": [1.0, 1.0, 1.0], + }, + } + ) + + # Architecture specifics + # Device to run the model on (e.g., "cuda", "cpu") + device: str = "cpu" + # Device to store the model on + storage_device: str = "cpu" + # Name of the vision encoder model (Set to "helper2424/resnet10" for hil serl resnet10) + vision_encoder_name: str | None = None + # Whether to freeze the vision encoder during training + freeze_vision_encoder: bool = True + # Hidden dimension size for the image encoder + image_encoder_hidden_dim: int = 32 + # Whether to use a shared encoder for actor and critic + shared_encoder: bool = True + # Number of discrete actions, eg for gripper actions + num_discrete_actions: int | None = None + # Dimension of the image embedding pooling + image_embedding_pooling_dim: int = 8 + + # Training parameter + # Number of steps for online training + online_steps: int = 1000000 + # Seed for the online environment + online_env_seed: int = 10000 + # Capacity of the online replay buffer + online_buffer_capacity: int = 100000 + # Capacity of the offline replay buffer + offline_buffer_capacity: int = 100000 + # Whether to use asynchronous prefetching for the buffers + async_prefetch: bool = False + # Number of steps before learning starts + online_step_before_learning: int = 100 + # Frequency of policy updates + policy_update_freq: int = 1 + + # SAC algorithm parameters + # Discount factor for the SAC algorithm + discount: float = 0.99 + # Initial temperature value + temperature_init: float = 1.0 + # Number of critics in the ensemble + num_critics: int = 2 + # Number of subsampled critics for training + num_subsample_critics: int | None = None + # Learning rate for the critic network + critic_lr: float = 3e-4 + # Learning rate for the actor network + actor_lr: float = 3e-4 + # Learning rate for the temperature parameter + temperature_lr: float = 3e-4 + # Weight for the critic target update + critic_target_update_weight: float = 0.005 + # Update-to-data ratio for the UTD algorithm (If you want enable utd_ratio, you need to set it to >1) + utd_ratio: int = 1 + # Hidden dimension size for the state encoder + state_encoder_hidden_dim: int = 256 + # Dimension of the latent space + latent_dim: int = 256 + # Target entropy for the SAC algorithm + target_entropy: float | None = None + # Whether to use backup entropy for the SAC algorithm + use_backup_entropy: bool = True + # Gradient clipping norm for the SAC algorithm + grad_clip_norm: float = 40.0 + + # Network configuration + # Configuration for the critic network architecture + critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig) + # Configuration for the actor network architecture + actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig) + # Configuration for the policy parameters + policy_kwargs: PolicyConfig = field(default_factory=PolicyConfig) + # Configuration for the discrete critic network + discrete_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig) + # Configuration for actor-learner architecture + actor_learner_config: ActorLearnerConfig = field(default_factory=ActorLearnerConfig) + # Configuration for concurrency settings (you can use threads or processes for the actor and learner) + concurrency: ConcurrencyConfig = field(default_factory=ConcurrencyConfig) + + # Optimizations + use_torch_compile: bool = True + + def __post_init__(self): + super().__post_init__() + # Any validation specific to SAC configuration + + def get_optimizer_preset(self) -> MultiAdamConfig: + return MultiAdamConfig( + weight_decay=0.0, + optimizer_groups={ + "actor": {"lr": self.actor_lr}, + "critic": {"lr": self.critic_lr}, + "temperature": {"lr": self.temperature_lr}, + }, + ) + + def get_scheduler_preset(self) -> None: + return None + + def validate_features(self) -> None: + has_image = any(is_image_feature(key) for key in self.input_features) + has_state = OBS_STATE in self.input_features + + if not (has_state or has_image): + raise ValueError( + "You must provide either 'observation.state' or an image observation (key starting with 'observation.image') in the input features" + ) + + if "action" not in self.output_features: + raise ValueError("You must provide 'action' in the output features") + + @property + def image_features(self) -> list[str]: + return [key for key in self.input_features if is_image_feature(key)] + + @property + def observation_delta_indices(self) -> list: + return None + + @property + def action_delta_indices(self) -> list: + return None # SAC typically predicts one action at a time + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/sac/modeling_sac.py b/lerobot/common/policies/sac/modeling_sac.py new file mode 100644 index 0000000000000000000000000000000000000000..379f3d037acee7ffed38c06cd5f9a26cd7611e5f --- /dev/null +++ b/lerobot/common/policies/sac/modeling_sac.py @@ -0,0 +1,1111 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import asdict +from typing import Callable, Literal + +import einops +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F # noqa: N812 +from torch import Tensor +from torch.distributions import MultivariateNormal, TanhTransform, Transform, TransformedDistribution + +from lerobot.common.policies.normalize import NormalizeBuffer +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.sac.configuration_sac import SACConfig, is_image_feature +from lerobot.common.policies.utils import get_device_from_parameters + +DISCRETE_DIMENSION_INDEX = -1 # Gripper is always the last dimension + + +class SACPolicy( + PreTrainedPolicy, +): + config_class = SACConfig + name = "sac" + + def __init__( + self, + config: SACConfig | None = None, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + super().__init__(config) + config.validate_features() + self.config = config + + # Determine action dimension and initialize all components + continuous_action_dim = config.output_features["action"].shape[0] + self._init_normalization(dataset_stats) + self._init_encoders() + self._init_critics(continuous_action_dim) + self._init_actor(continuous_action_dim) + self._init_temperature() + + def get_optim_params(self) -> dict: + optim_params = { + "actor": [ + p + for n, p in self.actor.named_parameters() + if not n.startswith("encoder") or not self.shared_encoder + ], + "critic": self.critic_ensemble.parameters(), + "temperature": self.log_alpha, + } + if self.config.num_discrete_actions is not None: + optim_params["discrete_critic"] = self.discrete_critic.parameters() + return optim_params + + def reset(self): + """Reset the policy""" + pass + + @torch.no_grad() + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select action for inference/evaluation""" + + observations_features = None + if self.shared_encoder and self.actor.encoder.has_images: + # Cache and normalize image features + observations_features = self.actor.encoder.get_cached_image_features(batch, normalize=True) + + actions, _, _ = self.actor(batch, observations_features) + + if self.config.num_discrete_actions is not None: + discrete_action_value = self.discrete_critic(batch, observations_features) + discrete_action = torch.argmax(discrete_action_value, dim=-1, keepdim=True) + actions = torch.cat([actions, discrete_action], dim=-1) + + return actions + + def critic_forward( + self, + observations: dict[str, Tensor], + actions: Tensor, + use_target: bool = False, + observation_features: Tensor | None = None, + ) -> Tensor: + """Forward pass through a critic network ensemble + + Args: + observations: Dictionary of observations + actions: Action tensor + use_target: If True, use target critics, otherwise use ensemble critics + + Returns: + Tensor of Q-values from all critics + """ + + critics = self.critic_target if use_target else self.critic_ensemble + q_values = critics(observations, actions, observation_features) + return q_values + + def discrete_critic_forward( + self, observations, use_target=False, observation_features=None + ) -> torch.Tensor: + """Forward pass through a discrete critic network + + Args: + observations: Dictionary of observations + use_target: If True, use target critics, otherwise use ensemble critics + observation_features: Optional pre-computed observation features to avoid recomputing encoder output + + Returns: + Tensor of Q-values from the discrete critic network + """ + discrete_critic = self.discrete_critic_target if use_target else self.discrete_critic + q_values = discrete_critic(observations, observation_features) + return q_values + + def forward( + self, + batch: dict[str, Tensor | dict[str, Tensor]], + model: Literal["actor", "critic", "temperature", "discrete_critic"] = "critic", + ) -> dict[str, Tensor]: + """Compute the loss for the given model + + Args: + batch: Dictionary containing: + - action: Action tensor + - reward: Reward tensor + - state: Observations tensor dict + - next_state: Next observations tensor dict + - done: Done mask tensor + - observation_feature: Optional pre-computed observation features + - next_observation_feature: Optional pre-computed next observation features + model: Which model to compute the loss for ("actor", "critic", "discrete_critic", or "temperature") + + Returns: + The computed loss tensor + """ + # Extract common components from batch + actions: Tensor = batch["action"] + observations: dict[str, Tensor] = batch["state"] + observation_features: Tensor = batch.get("observation_feature") + + if model == "critic": + # Extract critic-specific components + rewards: Tensor = batch["reward"] + next_observations: dict[str, Tensor] = batch["next_state"] + done: Tensor = batch["done"] + next_observation_features: Tensor = batch.get("next_observation_feature") + + loss_critic = self.compute_loss_critic( + observations=observations, + actions=actions, + rewards=rewards, + next_observations=next_observations, + done=done, + observation_features=observation_features, + next_observation_features=next_observation_features, + ) + + return {"loss_critic": loss_critic} + + if model == "discrete_critic" and self.config.num_discrete_actions is not None: + # Extract critic-specific components + rewards: Tensor = batch["reward"] + next_observations: dict[str, Tensor] = batch["next_state"] + done: Tensor = batch["done"] + next_observation_features: Tensor = batch.get("next_observation_feature") + complementary_info = batch.get("complementary_info") + loss_discrete_critic = self.compute_loss_discrete_critic( + observations=observations, + actions=actions, + rewards=rewards, + next_observations=next_observations, + done=done, + observation_features=observation_features, + next_observation_features=next_observation_features, + complementary_info=complementary_info, + ) + return {"loss_discrete_critic": loss_discrete_critic} + if model == "actor": + return { + "loss_actor": self.compute_loss_actor( + observations=observations, + observation_features=observation_features, + ) + } + + if model == "temperature": + return { + "loss_temperature": self.compute_loss_temperature( + observations=observations, + observation_features=observation_features, + ) + } + + raise ValueError(f"Unknown model type: {model}") + + def update_target_networks(self): + """Update target networks with exponential moving average""" + for target_param, param in zip( + self.critic_target.parameters(), + self.critic_ensemble.parameters(), + strict=True, + ): + target_param.data.copy_( + param.data * self.config.critic_target_update_weight + + target_param.data * (1.0 - self.config.critic_target_update_weight) + ) + if self.config.num_discrete_actions is not None: + for target_param, param in zip( + self.discrete_critic_target.parameters(), + self.discrete_critic.parameters(), + strict=True, + ): + target_param.data.copy_( + param.data * self.config.critic_target_update_weight + + target_param.data * (1.0 - self.config.critic_target_update_weight) + ) + + def update_temperature(self): + self.temperature = self.log_alpha.exp().item() + + def compute_loss_critic( + self, + observations, + actions, + rewards, + next_observations, + done, + observation_features: Tensor | None = None, + next_observation_features: Tensor | None = None, + ) -> Tensor: + with torch.no_grad(): + next_action_preds, next_log_probs, _ = self.actor(next_observations, next_observation_features) + + # 2- compute q targets + q_targets = self.critic_forward( + observations=next_observations, + actions=next_action_preds, + use_target=True, + observation_features=next_observation_features, + ) + + # subsample critics to prevent overfitting if use high UTD (update to date) + # TODO: Get indices before forward pass to avoid unnecessary computation + if self.config.num_subsample_critics is not None: + indices = torch.randperm(self.config.num_critics) + indices = indices[: self.config.num_subsample_critics] + q_targets = q_targets[indices] + + # critics subsample size + min_q, _ = q_targets.min(dim=0) # Get values from min operation + if self.config.use_backup_entropy: + min_q = min_q - (self.temperature * next_log_probs) + + td_target = rewards + (1 - done) * self.config.discount * min_q + + # 3- compute predicted qs + if self.config.num_discrete_actions is not None: + # NOTE: We only want to keep the continuous action part + # In the buffer we have the full action space (continuous + discrete) + # We need to split them before concatenating them in the critic forward + actions: Tensor = actions[:, :DISCRETE_DIMENSION_INDEX] + q_preds = self.critic_forward( + observations=observations, + actions=actions, + use_target=False, + observation_features=observation_features, + ) + + # 4- Calculate loss + # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble. + td_target_duplicate = einops.repeat(td_target, "b -> e b", e=q_preds.shape[0]) + # You compute the mean loss of the batch for each critic and then to compute the final loss you sum them up + critics_loss = ( + F.mse_loss( + input=q_preds, + target=td_target_duplicate, + reduction="none", + ).mean(dim=1) + ).sum() + return critics_loss + + def compute_loss_discrete_critic( + self, + observations, + actions, + rewards, + next_observations, + done, + observation_features=None, + next_observation_features=None, + complementary_info=None, + ): + # NOTE: We only want to keep the discrete action part + # In the buffer we have the full action space (continuous + discrete) + # We need to split them before concatenating them in the critic forward + actions_discrete: Tensor = actions[:, DISCRETE_DIMENSION_INDEX:].clone() + actions_discrete = torch.round(actions_discrete) + actions_discrete = actions_discrete.long() + + discrete_penalties: Tensor | None = None + if complementary_info is not None: + discrete_penalties: Tensor | None = complementary_info.get("discrete_penalty") + + with torch.no_grad(): + # For DQN, select actions using online network, evaluate with target network + next_discrete_qs = self.discrete_critic_forward( + next_observations, use_target=False, observation_features=next_observation_features + ) + best_next_discrete_action = torch.argmax(next_discrete_qs, dim=-1, keepdim=True) + + # Get target Q-values from target network + target_next_discrete_qs = self.discrete_critic_forward( + observations=next_observations, + use_target=True, + observation_features=next_observation_features, + ) + + # Use gather to select Q-values for best actions + target_next_discrete_q = torch.gather( + target_next_discrete_qs, dim=1, index=best_next_discrete_action + ).squeeze(-1) + + # Compute target Q-value with Bellman equation + rewards_discrete = rewards + if discrete_penalties is not None: + rewards_discrete = rewards + discrete_penalties + target_discrete_q = rewards_discrete + (1 - done) * self.config.discount * target_next_discrete_q + + # Get predicted Q-values for current observations + predicted_discrete_qs = self.discrete_critic_forward( + observations=observations, use_target=False, observation_features=observation_features + ) + + # Use gather to select Q-values for taken actions + predicted_discrete_q = torch.gather(predicted_discrete_qs, dim=1, index=actions_discrete).squeeze(-1) + + # Compute MSE loss between predicted and target Q-values + discrete_critic_loss = F.mse_loss(input=predicted_discrete_q, target=target_discrete_q) + return discrete_critic_loss + + def compute_loss_temperature(self, observations, observation_features: Tensor | None = None) -> Tensor: + """Compute the temperature loss""" + # calculate temperature loss + with torch.no_grad(): + _, log_probs, _ = self.actor(observations, observation_features) + temperature_loss = (-self.log_alpha.exp() * (log_probs + self.target_entropy)).mean() + return temperature_loss + + def compute_loss_actor( + self, + observations, + observation_features: Tensor | None = None, + ) -> Tensor: + actions_pi, log_probs, _ = self.actor(observations, observation_features) + + q_preds = self.critic_forward( + observations=observations, + actions=actions_pi, + use_target=False, + observation_features=observation_features, + ) + min_q_preds = q_preds.min(dim=0)[0] + + actor_loss = ((self.temperature * log_probs) - min_q_preds).mean() + return actor_loss + + def _init_normalization(self, dataset_stats): + """Initialize input/output normalization modules.""" + self.normalize_inputs = nn.Identity() + self.normalize_targets = nn.Identity() + if self.config.dataset_stats is not None: + params = _convert_normalization_params_to_tensor(self.config.dataset_stats) + self.normalize_inputs = NormalizeBuffer( + self.config.input_features, self.config.normalization_mapping, params + ) + stats = dataset_stats or params + self.normalize_targets = NormalizeBuffer( + self.config.output_features, self.config.normalization_mapping, stats + ) + + def _init_encoders(self): + """Initialize shared or separate encoders for actor and critic.""" + self.shared_encoder = self.config.shared_encoder + self.encoder_critic = SACObservationEncoder(self.config, self.normalize_inputs) + self.encoder_actor = ( + self.encoder_critic + if self.shared_encoder + else SACObservationEncoder(self.config, self.normalize_inputs) + ) + + def _init_critics(self, continuous_action_dim): + """Build critic ensemble, targets, and optional discrete critic.""" + heads = [ + CriticHead( + input_dim=self.encoder_critic.output_dim + continuous_action_dim, + **asdict(self.config.critic_network_kwargs), + ) + for _ in range(self.config.num_critics) + ] + self.critic_ensemble = CriticEnsemble( + encoder=self.encoder_critic, ensemble=heads, output_normalization=self.normalize_targets + ) + target_heads = [ + CriticHead( + input_dim=self.encoder_critic.output_dim + continuous_action_dim, + **asdict(self.config.critic_network_kwargs), + ) + for _ in range(self.config.num_critics) + ] + self.critic_target = CriticEnsemble( + encoder=self.encoder_critic, ensemble=target_heads, output_normalization=self.normalize_targets + ) + self.critic_target.load_state_dict(self.critic_ensemble.state_dict()) + + if self.config.use_torch_compile: + self.critic_ensemble = torch.compile(self.critic_ensemble) + self.critic_target = torch.compile(self.critic_target) + + if self.config.num_discrete_actions is not None: + self._init_discrete_critics() + + def _init_discrete_critics(self): + """Build discrete discrete critic ensemble and target networks.""" + self.discrete_critic = DiscreteCritic( + encoder=self.encoder_critic, + input_dim=self.encoder_critic.output_dim, + output_dim=self.config.num_discrete_actions, + **asdict(self.config.discrete_critic_network_kwargs), + ) + self.discrete_critic_target = DiscreteCritic( + encoder=self.encoder_critic, + input_dim=self.encoder_critic.output_dim, + output_dim=self.config.num_discrete_actions, + **asdict(self.config.discrete_critic_network_kwargs), + ) + + # TODO: (maractingi, azouitine) Compile the discrete critic + self.discrete_critic_target.load_state_dict(self.discrete_critic.state_dict()) + + def _init_actor(self, continuous_action_dim): + """Initialize policy actor network and default target entropy.""" + # NOTE: The actor select only the continuous action part + self.actor = Policy( + encoder=self.encoder_actor, + network=MLP(input_dim=self.encoder_actor.output_dim, **asdict(self.config.actor_network_kwargs)), + action_dim=continuous_action_dim, + encoder_is_shared=self.shared_encoder, + **asdict(self.config.policy_kwargs), + ) + + self.target_entropy = self.config.target_entropy + if self.target_entropy is None: + dim = continuous_action_dim + (1 if self.config.num_discrete_actions is not None else 0) + self.target_entropy = -np.prod(dim) / 2 + + def _init_temperature(self): + """Set up temperature parameter and initial log_alpha.""" + temp_init = self.config.temperature_init + self.log_alpha = nn.Parameter(torch.tensor([math.log(temp_init)])) + self.temperature = self.log_alpha.exp().item() + + +class SACObservationEncoder(nn.Module): + """Encode image and/or state vector observations.""" + + def __init__(self, config: SACConfig, input_normalizer: nn.Module) -> None: + super().__init__() + self.config = config + self.input_normalization = input_normalizer + self._init_image_layers() + self._init_state_layers() + self._compute_output_dim() + + def _init_image_layers(self) -> None: + self.image_keys = [k for k in self.config.input_features if is_image_feature(k)] + self.has_images = bool(self.image_keys) + if not self.has_images: + return + + if self.config.vision_encoder_name is not None: + self.image_encoder = PretrainedImageEncoder(self.config) + else: + self.image_encoder = DefaultImageEncoder(self.config) + + if self.config.freeze_vision_encoder: + freeze_image_encoder(self.image_encoder) + + dummy = torch.zeros(1, *self.config.input_features[self.image_keys[0]].shape) + with torch.no_grad(): + _, channels, height, width = self.image_encoder(dummy).shape + + self.spatial_embeddings = nn.ModuleDict() + self.post_encoders = nn.ModuleDict() + + for key in self.image_keys: + name = key.replace(".", "_") + self.spatial_embeddings[name] = SpatialLearnedEmbeddings( + height=height, + width=width, + channel=channels, + num_features=self.config.image_embedding_pooling_dim, + ) + self.post_encoders[name] = nn.Sequential( + nn.Dropout(0.1), + nn.Linear( + in_features=channels * self.config.image_embedding_pooling_dim, + out_features=self.config.latent_dim, + ), + nn.LayerNorm(normalized_shape=self.config.latent_dim), + nn.Tanh(), + ) + + def _init_state_layers(self) -> None: + self.has_env = "observation.environment_state" in self.config.input_features + self.has_state = "observation.state" in self.config.input_features + if self.has_env: + dim = self.config.input_features["observation.environment_state"].shape[0] + self.env_encoder = nn.Sequential( + nn.Linear(dim, self.config.latent_dim), + nn.LayerNorm(self.config.latent_dim), + nn.Tanh(), + ) + if self.has_state: + dim = self.config.input_features["observation.state"].shape[0] + self.state_encoder = nn.Sequential( + nn.Linear(dim, self.config.latent_dim), + nn.LayerNorm(self.config.latent_dim), + nn.Tanh(), + ) + + def _compute_output_dim(self) -> None: + out = 0 + if self.has_images: + out += len(self.image_keys) * self.config.latent_dim + if self.has_env: + out += self.config.latent_dim + if self.has_state: + out += self.config.latent_dim + self._out_dim = out + + def forward( + self, obs: dict[str, Tensor], cache: dict[str, Tensor] | None = None, detach: bool = False + ) -> Tensor: + obs = self.input_normalization(obs) + parts = [] + if self.has_images: + if cache is None: + cache = self.get_cached_image_features(obs, normalize=False) + parts.append(self._encode_images(cache, detach)) + if self.has_env: + parts.append(self.env_encoder(obs["observation.environment_state"])) + if self.has_state: + parts.append(self.state_encoder(obs["observation.state"])) + if parts: + return torch.cat(parts, dim=-1) + + raise ValueError( + "No parts to concatenate, you should have at least one image or environment state or state" + ) + + def get_cached_image_features(self, obs: dict[str, Tensor], normalize: bool = False) -> dict[str, Tensor]: + """Extract and optionally cache image features from observations. + + This function processes image observations through the vision encoder once and returns + the resulting features. + When the image encoder is shared between actor and critics AND frozen, these features can be safely cached and + reused across policy components (actor, critic, discrete_critic), avoiding redundant forward passes. + + Performance impact: + - The vision encoder forward pass is typically the main computational bottleneck during training and inference + - Caching these features can provide 2-4x speedup in training and inference + + Normalization behavior: + - When called from inside forward(): set normalize=False since inputs are already normalized + - When called from outside forward(): set normalize=True to ensure proper input normalization + + Usage patterns: + - Called in select_action() with normalize=True + - Called in learner.py's get_observation_features() to pre-compute features for all policy components + - Called internally by forward() with normalize=False + + Args: + obs: Dictionary of observation tensors containing image keys + normalize: Whether to normalize observations before encoding + Set to True when calling directly from outside the encoder's forward method + Set to False when calling from within forward() where inputs are already normalized + + Returns: + Dictionary mapping image keys to their corresponding encoded features + """ + if normalize: + obs = self.input_normalization(obs) + batched = torch.cat([obs[k] for k in self.image_keys], dim=0) + out = self.image_encoder(batched) + chunks = torch.chunk(out, len(self.image_keys), dim=0) + return dict(zip(self.image_keys, chunks, strict=False)) + + def _encode_images(self, cache: dict[str, Tensor], detach: bool) -> Tensor: + """Encode image features from cached observations. + + This function takes pre-encoded image features from the cache and applies spatial embeddings and post-encoders. + It also supports detaching the encoded features if specified. + + Args: + cache (dict[str, Tensor]): The cached image features. + detach (bool): Usually when the encoder is shared between actor and critics, + we want to detach the encoded features on the policy side to avoid backprop through the encoder. + More detail here `https://cdn.aaai.org/ojs/17276/17276-13-20770-1-2-20210518.pdf` + + Returns: + Tensor: The encoded image features. + """ + feats = [] + for k, feat in cache.items(): + safe_key = k.replace(".", "_") + x = self.spatial_embeddings[safe_key](feat) + x = self.post_encoders[safe_key](x) + if detach: + x = x.detach() + feats.append(x) + return torch.cat(feats, dim=-1) + + @property + def output_dim(self) -> int: + return self._out_dim + + +class MLP(nn.Module): + """Multi-layer perceptron builder. + + Dynamically constructs a sequence of layers based on `hidden_dims`: + 1) Linear (in_dim -> out_dim) + 2) Optional Dropout if `dropout_rate` > 0 and (not final layer or `activate_final`) + 3) LayerNorm on the output features + 4) Activation (standard for intermediate layers, `final_activation` for last layer if `activate_final`) + + Arguments: + input_dim (int): Size of input feature dimension. + hidden_dims (list[int]): Sizes for each hidden layer. + activations (Callable or str): Activation to apply between layers. + activate_final (bool): Whether to apply activation at the final layer. + dropout_rate (Optional[float]): Dropout probability applied before normalization and activation. + final_activation (Optional[Callable or str]): Activation for the final layer when `activate_final` is True. + + For each layer, `in_dim` is updated to the previous `out_dim`. All constructed modules are + stored in `self.net` as an `nn.Sequential` container. + """ + + def __init__( + self, + input_dim: int, + hidden_dims: list[int], + activations: Callable[[torch.Tensor], torch.Tensor] | str = nn.SiLU(), + activate_final: bool = False, + dropout_rate: float | None = None, + final_activation: Callable[[torch.Tensor], torch.Tensor] | str | None = None, + ): + super().__init__() + layers: list[nn.Module] = [] + in_dim = input_dim + total = len(hidden_dims) + + for idx, out_dim in enumerate(hidden_dims): + # 1) linear transform + layers.append(nn.Linear(in_dim, out_dim)) + + is_last = idx == total - 1 + # 2-4) optionally add dropout, normalization, and activation + if not is_last or activate_final: + if dropout_rate and dropout_rate > 0: + layers.append(nn.Dropout(p=dropout_rate)) + layers.append(nn.LayerNorm(out_dim)) + act_cls = final_activation if is_last and final_activation else activations + act = act_cls if isinstance(act_cls, nn.Module) else getattr(nn, act_cls)() + layers.append(act) + + in_dim = out_dim + + self.net = nn.Sequential(*layers) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) + + +class CriticHead(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dims: list[int], + activations: Callable[[torch.Tensor], torch.Tensor] | str = nn.SiLU(), + activate_final: bool = False, + dropout_rate: float | None = None, + init_final: float | None = None, + final_activation: Callable[[torch.Tensor], torch.Tensor] | str | None = None, + ): + super().__init__() + self.net = MLP( + input_dim=input_dim, + hidden_dims=hidden_dims, + activations=activations, + activate_final=activate_final, + dropout_rate=dropout_rate, + final_activation=final_activation, + ) + self.output_layer = nn.Linear(in_features=hidden_dims[-1], out_features=1) + if init_final is not None: + nn.init.uniform_(self.output_layer.weight, -init_final, init_final) + nn.init.uniform_(self.output_layer.bias, -init_final, init_final) + else: + orthogonal_init()(self.output_layer.weight) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.output_layer(self.net(x)) + + +class CriticEnsemble(nn.Module): + """ + CriticEnsemble wraps multiple CriticHead modules into an ensemble. + + Args: + encoder (SACObservationEncoder): encoder for observations. + ensemble (List[CriticHead]): list of critic heads. + output_normalization (nn.Module): normalization layer for actions. + init_final (float | None): optional initializer scale for final layers. + + Forward returns a tensor of shape (num_critics, batch_size) containing Q-values. + """ + + def __init__( + self, + encoder: SACObservationEncoder, + ensemble: list[CriticHead], + output_normalization: nn.Module, + init_final: float | None = None, + ): + super().__init__() + self.encoder = encoder + self.init_final = init_final + self.output_normalization = output_normalization + self.critics = nn.ModuleList(ensemble) + + def forward( + self, + observations: dict[str, torch.Tensor], + actions: torch.Tensor, + observation_features: torch.Tensor | None = None, + ) -> torch.Tensor: + device = get_device_from_parameters(self) + # Move each tensor in observations to device + observations = {k: v.to(device) for k, v in observations.items()} + # NOTE: We normalize actions it helps for sample efficiency + actions: dict[str, torch.tensor] = {"action": actions} + # NOTE: Normalization layer took dict in input and outputs a dict that why + actions = self.output_normalization(actions)["action"] + actions = actions.to(device) + + obs_enc = self.encoder(observations, cache=observation_features) + + inputs = torch.cat([obs_enc, actions], dim=-1) + + # Loop through critics and collect outputs + q_values = [] + for critic in self.critics: + q_values.append(critic(inputs)) + + # Stack outputs to match expected shape [num_critics, batch_size] + q_values = torch.stack([q.squeeze(-1) for q in q_values], dim=0) + return q_values + + +class DiscreteCritic(nn.Module): + def __init__( + self, + encoder: nn.Module, + input_dim: int, + hidden_dims: list[int], + output_dim: int = 3, + activations: Callable[[torch.Tensor], torch.Tensor] | str = nn.SiLU(), + activate_final: bool = False, + dropout_rate: float | None = None, + init_final: float | None = None, + final_activation: Callable[[torch.Tensor], torch.Tensor] | str | None = None, + ): + super().__init__() + self.encoder = encoder + self.output_dim = output_dim + + self.net = MLP( + input_dim=input_dim, + hidden_dims=hidden_dims, + activations=activations, + activate_final=activate_final, + dropout_rate=dropout_rate, + final_activation=final_activation, + ) + + self.output_layer = nn.Linear(in_features=hidden_dims[-1], out_features=self.output_dim) + if init_final is not None: + nn.init.uniform_(self.output_layer.weight, -init_final, init_final) + nn.init.uniform_(self.output_layer.bias, -init_final, init_final) + else: + orthogonal_init()(self.output_layer.weight) + + def forward( + self, observations: torch.Tensor, observation_features: torch.Tensor | None = None + ) -> torch.Tensor: + device = get_device_from_parameters(self) + observations = {k: v.to(device) for k, v in observations.items()} + obs_enc = self.encoder(observations, cache=observation_features) + return self.output_layer(self.net(obs_enc)) + + +class Policy(nn.Module): + def __init__( + self, + encoder: SACObservationEncoder, + network: nn.Module, + action_dim: int, + std_min: float = -5, + std_max: float = 2, + fixed_std: torch.Tensor | None = None, + init_final: float | None = None, + use_tanh_squash: bool = False, + encoder_is_shared: bool = False, + ): + super().__init__() + self.encoder: SACObservationEncoder = encoder + self.network = network + self.action_dim = action_dim + self.std_min = std_min + self.std_max = std_max + self.fixed_std = fixed_std + self.use_tanh_squash = use_tanh_squash + self.encoder_is_shared = encoder_is_shared + + # Find the last Linear layer's output dimension + for layer in reversed(network.net): + if isinstance(layer, nn.Linear): + out_features = layer.out_features + break + # Mean layer + self.mean_layer = nn.Linear(out_features, action_dim) + if init_final is not None: + nn.init.uniform_(self.mean_layer.weight, -init_final, init_final) + nn.init.uniform_(self.mean_layer.bias, -init_final, init_final) + else: + orthogonal_init()(self.mean_layer.weight) + + # Standard deviation layer or parameter + if fixed_std is None: + self.std_layer = nn.Linear(out_features, action_dim) + if init_final is not None: + nn.init.uniform_(self.std_layer.weight, -init_final, init_final) + nn.init.uniform_(self.std_layer.bias, -init_final, init_final) + else: + orthogonal_init()(self.std_layer.weight) + + def forward( + self, + observations: torch.Tensor, + observation_features: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + # We detach the encoder if it is shared to avoid backprop through it + # This is important to avoid the encoder to be updated through the policy + obs_enc = self.encoder(observations, cache=observation_features, detach=self.encoder_is_shared) + + # Get network outputs + outputs = self.network(obs_enc) + means = self.mean_layer(outputs) + + # Compute standard deviations + if self.fixed_std is None: + log_std = self.std_layer(outputs) + std = torch.exp(log_std) # Match JAX "exp" + std = torch.clamp(std, self.std_min, self.std_max) # Match JAX default clip + else: + std = self.fixed_std.expand_as(means) + + # Build transformed distribution + dist = TanhMultivariateNormalDiag(loc=means, scale_diag=std) + + # Sample actions (reparameterized) + actions = dist.rsample() + + # Compute log_probs + log_probs = dist.log_prob(actions) + + return actions, log_probs, means + + def get_features(self, observations: torch.Tensor) -> torch.Tensor: + """Get encoded features from observations""" + device = get_device_from_parameters(self) + observations = observations.to(device) + if self.encoder is not None: + with torch.inference_mode(): + return self.encoder(observations) + return observations + + +class DefaultImageEncoder(nn.Module): + def __init__(self, config: SACConfig): + super().__init__() + image_key = next(key for key in config.input_features if is_image_feature(key)) + self.image_enc_layers = nn.Sequential( + nn.Conv2d( + in_channels=config.input_features[image_key].shape[0], + out_channels=config.image_encoder_hidden_dim, + kernel_size=7, + stride=2, + ), + nn.ReLU(), + nn.Conv2d( + in_channels=config.image_encoder_hidden_dim, + out_channels=config.image_encoder_hidden_dim, + kernel_size=5, + stride=2, + ), + nn.ReLU(), + nn.Conv2d( + in_channels=config.image_encoder_hidden_dim, + out_channels=config.image_encoder_hidden_dim, + kernel_size=3, + stride=2, + ), + nn.ReLU(), + nn.Conv2d( + in_channels=config.image_encoder_hidden_dim, + out_channels=config.image_encoder_hidden_dim, + kernel_size=3, + stride=2, + ), + nn.ReLU(), + ) + + def forward(self, x): + x = self.image_enc_layers(x) + return x + + +def freeze_image_encoder(image_encoder: nn.Module): + """Freeze all parameters in the encoder""" + for param in image_encoder.parameters(): + param.requires_grad = False + + +class PretrainedImageEncoder(nn.Module): + def __init__(self, config: SACConfig): + super().__init__() + + self.image_enc_layers, self.image_enc_out_shape = self._load_pretrained_vision_encoder(config) + + def _load_pretrained_vision_encoder(self, config: SACConfig): + """Set up CNN encoder""" + from transformers import AutoModel + + self.image_enc_layers = AutoModel.from_pretrained(config.vision_encoder_name, trust_remote_code=True) + + if hasattr(self.image_enc_layers.config, "hidden_sizes"): + self.image_enc_out_shape = self.image_enc_layers.config.hidden_sizes[-1] # Last channel dimension + elif hasattr(self.image_enc_layers, "fc"): + self.image_enc_out_shape = self.image_enc_layers.fc.in_features + else: + raise ValueError("Unsupported vision encoder architecture, make sure you are using a CNN") + return self.image_enc_layers, self.image_enc_out_shape + + def forward(self, x): + enc_feat = self.image_enc_layers(x).last_hidden_state + return enc_feat + + +def orthogonal_init(): + return lambda x: torch.nn.init.orthogonal_(x, gain=1.0) + + +class SpatialLearnedEmbeddings(nn.Module): + def __init__(self, height, width, channel, num_features=8): + """ + PyTorch implementation of learned spatial embeddings + + Args: + height: Spatial height of input features + width: Spatial width of input features + channel: Number of input channels + num_features: Number of output embedding dimensions + """ + super().__init__() + self.height = height + self.width = width + self.channel = channel + self.num_features = num_features + + self.kernel = nn.Parameter(torch.empty(channel, height, width, num_features)) + + nn.init.kaiming_normal_(self.kernel, mode="fan_in", nonlinearity="linear") + + def forward(self, features): + """ + Forward pass for spatial embedding + + Args: + features: Input tensor of shape [B, C, H, W] where B is batch size, + C is number of channels, H is height, and W is width + Returns: + Output tensor of shape [B, C*F] where F is the number of features + """ + + features_expanded = features.unsqueeze(-1) # [B, C, H, W, 1] + kernel_expanded = self.kernel.unsqueeze(0) # [1, C, H, W, F] + + # Element-wise multiplication and spatial reduction + output = (features_expanded * kernel_expanded).sum(dim=(2, 3)) # Sum over H,W dimensions + + # Reshape to combine channel and feature dimensions + output = output.view(output.size(0), -1) # [B, C*F] + + return output + + +class RescaleFromTanh(Transform): + def __init__(self, low: float = -1, high: float = 1): + super().__init__() + + self.low = low + + self.high = high + + def _call(self, x): + # Rescale from (-1, 1) to (low, high) + + return 0.5 * (x + 1.0) * (self.high - self.low) + self.low + + def _inverse(self, y): + # Rescale from (low, high) back to (-1, 1) + + return 2.0 * (y - self.low) / (self.high - self.low) - 1.0 + + def log_abs_det_jacobian(self, x, y): + # log|d(rescale)/dx| = sum(log(0.5 * (high - low))) + + scale = 0.5 * (self.high - self.low) + + return torch.sum(torch.log(scale), dim=-1) + + +class TanhMultivariateNormalDiag(TransformedDistribution): + def __init__(self, loc, scale_diag, low=None, high=None): + base_dist = MultivariateNormal(loc, torch.diag_embed(scale_diag)) + + transforms = [TanhTransform(cache_size=1)] + + if low is not None and high is not None: + low = torch.as_tensor(low) + + high = torch.as_tensor(high) + + transforms.insert(0, RescaleFromTanh(low, high)) + + super().__init__(base_dist, transforms) + + def mode(self): + # Mode is mean of base distribution, passed through transforms + + x = self.base_dist.mean + + for transform in self.transforms: + x = transform(x) + + return x + + def stddev(self): + std = self.base_dist.stddev + + x = std + + for transform in self.transforms: + x = transform(x) + + return x + + +def _convert_normalization_params_to_tensor(normalization_params: dict) -> dict: + converted_params = {} + for outer_key, inner_dict in normalization_params.items(): + converted_params[outer_key] = {} + for key, value in inner_dict.items(): + converted_params[outer_key][key] = torch.tensor(value) + if "image" in outer_key: + converted_params[outer_key][key] = converted_params[outer_key][key].view(3, 1, 1) + + return converted_params diff --git a/lerobot/common/policies/sac/reward_model/configuration_classifier.py b/lerobot/common/policies/sac/reward_model/configuration_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..923a2ae0b2db39114503329f1115634e85a5bacb --- /dev/null +++ b/lerobot/common/policies/sac/reward_model/configuration_classifier.py @@ -0,0 +1,76 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamWConfig, OptimizerConfig +from lerobot.common.optim.schedulers import LRSchedulerConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import NormalizationMode + + +@PreTrainedConfig.register_subclass(name="reward_classifier") +@dataclass +class RewardClassifierConfig(PreTrainedConfig): + """Configuration for the Reward Classifier model.""" + + name: str = "reward_classifier" + num_classes: int = 2 + hidden_dim: int = 256 + latent_dim: int = 256 + image_embedding_pooling_dim: int = 8 + dropout_rate: float = 0.1 + model_name: str = "helper2424/resnet10" + device: str = "cpu" + model_type: str = "cnn" # "transformer" or "cnn" + num_cameras: int = 2 + learning_rate: float = 1e-4 + weight_decay: float = 0.01 + grad_clip_norm: float = 1.0 + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.MEAN_STD, + } + ) + + @property + def observation_delta_indices(self) -> list | None: + return None + + @property + def action_delta_indices(self) -> list | None: + return None + + @property + def reward_delta_indices(self) -> list | None: + return None + + def get_optimizer_preset(self) -> OptimizerConfig: + return AdamWConfig( + lr=self.learning_rate, + weight_decay=self.weight_decay, + grad_clip_norm=self.grad_clip_norm, + ) + + def get_scheduler_preset(self) -> LRSchedulerConfig | None: + return None + + def validate_features(self) -> None: + """Validate feature configurations.""" + has_image = any(key.startswith("observation.image") for key in self.input_features) + if not has_image: + raise ValueError( + "You must provide an image observation (key starting with 'observation.image') in the input features" + ) diff --git a/lerobot/common/policies/sac/reward_model/modeling_classifier.py b/lerobot/common/policies/sac/reward_model/modeling_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..eceffeca17569b6d37378dd05bc0dd10993bd802 --- /dev/null +++ b/lerobot/common/policies/sac/reward_model/modeling_classifier.py @@ -0,0 +1,316 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import torch +from torch import Tensor, nn + +from lerobot.common.constants import OBS_IMAGE, REWARD +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig + + +class ClassifierOutput: + """Wrapper for classifier outputs with additional metadata.""" + + def __init__( + self, + logits: Tensor, + probabilities: Tensor | None = None, + hidden_states: Tensor | None = None, + ): + self.logits = logits + self.probabilities = probabilities + self.hidden_states = hidden_states + + def __repr__(self): + return ( + f"ClassifierOutput(logits={self.logits}, " + f"probabilities={self.probabilities}, " + f"hidden_states={self.hidden_states})" + ) + + +class SpatialLearnedEmbeddings(nn.Module): + def __init__(self, height, width, channel, num_features=8): + """ + PyTorch implementation of learned spatial embeddings + + Args: + height: Spatial height of input features + width: Spatial width of input features + channel: Number of input channels + num_features: Number of output embedding dimensions + """ + super().__init__() + self.height = height + self.width = width + self.channel = channel + self.num_features = num_features + + self.kernel = nn.Parameter(torch.empty(channel, height, width, num_features)) + + nn.init.kaiming_normal_(self.kernel, mode="fan_in", nonlinearity="linear") + + def forward(self, features): + """ + Forward pass for spatial embedding + + Args: + features: Input tensor of shape [B, H, W, C] or [H, W, C] if no batch + Returns: + Output tensor of shape [B, C*F] or [C*F] if no batch + """ + + features = features.last_hidden_state + + original_shape = features.shape + if features.dim() == 3: + features = features.unsqueeze(0) # Add batch dim + + features_expanded = features.unsqueeze(-1) # [B, H, W, C, 1] + kernel_expanded = self.kernel.unsqueeze(0) # [1, H, W, C, F] + + # Element-wise multiplication and spatial reduction + output = (features_expanded * kernel_expanded).sum(dim=(2, 3)) # Sum H,W + + # Reshape to combine channel and feature dimensions + output = output.view(output.size(0), -1) # [B, C*F] + + # Remove batch dim + if len(original_shape) == 3: + output = output.squeeze(0) + + return output + + +class Classifier(PreTrainedPolicy): + """Image classifier built on top of a pre-trained encoder.""" + + name = "reward_classifier" + config_class = RewardClassifierConfig + + def __init__( + self, + config: RewardClassifierConfig, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + from transformers import AutoModel + + super().__init__(config) + self.config = config + + # Initialize normalization (standardized with the policy framework) + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + # Set up encoder + encoder = AutoModel.from_pretrained(self.config.model_name, trust_remote_code=True) + # Extract vision model if we're given a multimodal model + if hasattr(encoder, "vision_model"): + logging.info("Multimodal model detected - using vision encoder only") + self.encoder = encoder.vision_model + self.vision_config = encoder.config.vision_config + else: + self.encoder = encoder + self.vision_config = getattr(encoder, "config", None) + + # Model type from config + self.is_cnn = self.config.model_type == "cnn" + + # For CNNs, initialize backbone + if self.is_cnn: + self._setup_cnn_backbone() + + self._freeze_encoder() + + # Extract image keys from input_features + self.image_keys = [ + key.replace(".", "_") for key in config.input_features if key.startswith(OBS_IMAGE) + ] + + if self.is_cnn: + self.encoders = nn.ModuleDict() + for image_key in self.image_keys: + encoder = self._create_single_encoder() + self.encoders[image_key] = encoder + + self._build_classifier_head() + + def _setup_cnn_backbone(self): + """Set up CNN encoder""" + if hasattr(self.encoder, "fc"): + self.feature_dim = self.encoder.fc.in_features + self.encoder = nn.Sequential(*list(self.encoder.children())[:-1]) + elif hasattr(self.encoder.config, "hidden_sizes"): + self.feature_dim = self.encoder.config.hidden_sizes[-1] # Last channel dimension + else: + raise ValueError("Unsupported CNN architecture") + + def _freeze_encoder(self) -> None: + """Freeze the encoder parameters.""" + for param in self.encoder.parameters(): + param.requires_grad = False + + def _create_single_encoder(self): + encoder = nn.Sequential( + self.encoder, + SpatialLearnedEmbeddings( + height=4, + width=4, + channel=self.feature_dim, + num_features=self.config.image_embedding_pooling_dim, + ), + nn.Dropout(self.config.dropout_rate), + nn.Linear(self.feature_dim * self.config.image_embedding_pooling_dim, self.config.latent_dim), + nn.LayerNorm(self.config.latent_dim), + nn.Tanh(), + ) + + return encoder + + def _build_classifier_head(self) -> None: + """Initialize the classifier head architecture.""" + # Get input dimension based on model type + if self.is_cnn: + input_dim = self.config.latent_dim + else: # Transformer models + if hasattr(self.encoder.config, "hidden_size"): + input_dim = self.encoder.config.hidden_size + else: + raise ValueError("Unsupported transformer architecture since hidden_size is not found") + + self.classifier_head = nn.Sequential( + nn.Linear(input_dim * self.config.num_cameras, self.config.hidden_dim), + nn.Dropout(self.config.dropout_rate), + nn.LayerNorm(self.config.hidden_dim), + nn.ReLU(), + nn.Linear( + self.config.hidden_dim, + 1 if self.config.num_classes == 2 else self.config.num_classes, + ), + ) + + def _get_encoder_output(self, x: torch.Tensor, image_key: str) -> torch.Tensor: + """Extract the appropriate output from the encoder.""" + with torch.no_grad(): + if self.is_cnn: + # The HF ResNet applies pooling internally + outputs = self.encoders[image_key](x) + return outputs + else: # Transformer models + outputs = self.encoder(x) + return outputs.last_hidden_state[:, 0, :] + + def extract_images_and_labels(self, batch: dict[str, Tensor]) -> tuple[list, Tensor]: + """Extract image tensors and label tensors from batch.""" + # Check for both OBS_IMAGE and OBS_IMAGES prefixes + images = [batch[key] for key in self.config.input_features if key.startswith(OBS_IMAGE)] + labels = batch[REWARD] + + return images, labels + + def predict(self, xs: list) -> ClassifierOutput: + """Forward pass of the classifier for inference.""" + encoder_outputs = torch.hstack( + [self._get_encoder_output(x, img_key) for x, img_key in zip(xs, self.image_keys, strict=True)] + ) + logits = self.classifier_head(encoder_outputs) + + if self.config.num_classes == 2: + logits = logits.squeeze(-1) + probabilities = torch.sigmoid(logits) + else: + probabilities = torch.softmax(logits, dim=-1) + + return ClassifierOutput(logits=logits, probabilities=probabilities, hidden_states=encoder_outputs) + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Tensor]]: + """Standard forward pass for training compatible with train.py.""" + # Normalize inputs if needed + batch = self.normalize_inputs(batch) + batch = self.normalize_targets(batch) + + # Extract images and labels + images, labels = self.extract_images_and_labels(batch) + + # Get predictions + outputs = self.predict(images) + + # Calculate loss + if self.config.num_classes == 2: + # Binary classification + loss = nn.functional.binary_cross_entropy_with_logits(outputs.logits, labels) + predictions = (torch.sigmoid(outputs.logits) > 0.5).float() + else: + # Multi-class classification + loss = nn.functional.cross_entropy(outputs.logits, labels.long()) + predictions = torch.argmax(outputs.logits, dim=1) + + # Calculate accuracy for logging + correct = (predictions == labels).sum().item() + total = labels.size(0) + accuracy = 100 * correct / total + + # Return loss and metrics for logging + output_dict = { + "accuracy": accuracy, + "correct": correct, + "total": total, + } + + return loss, output_dict + + def predict_reward(self, batch, threshold=0.5): + """Eval method. Returns predicted reward with the decision threshold as argument.""" + # Check for both OBS_IMAGE and OBS_IMAGES prefixes + batch = self.normalize_inputs(batch) + batch = self.normalize_targets(batch) + + # Extract images from batch dict + images = [batch[key] for key in self.config.input_features if key.startswith(OBS_IMAGE)] + + if self.config.num_classes == 2: + probs = self.predict(images).probabilities + logging.debug(f"Predicted reward images: {probs}") + return (probs > threshold).float() + else: + return torch.argmax(self.predict(images).probabilities, dim=1) + + def get_optim_params(self): + """Return optimizer parameters for the policy.""" + return self.parameters() + + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """ + This method is required by PreTrainedPolicy but not used for reward classifiers. + The reward classifier is not an actor and does not select actions. + """ + raise NotImplementedError("Reward classifiers do not select actions") + + def reset(self): + """ + This method is required by PreTrainedPolicy but not used for reward classifiers. + The reward classifier is not an actor and does not select actions. + """ + pass diff --git a/lerobot/common/policies/smolvla/configuration_smolvla.py b/lerobot/common/policies/smolvla/configuration_smolvla.py new file mode 100644 index 0000000000000000000000000000000000000000..7bb28a78ba8a29cd979ae9f0b573f21901ddd3b2 --- /dev/null +++ b/lerobot/common/policies/smolvla/configuration_smolvla.py @@ -0,0 +1,154 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamWConfig +from lerobot.common.optim.schedulers import ( + CosineDecayWithWarmupSchedulerConfig, +) +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature + + +@PreTrainedConfig.register_subclass("smolvla") +@dataclass +class SmolVLAConfig(PreTrainedConfig): + # Input / output structure. + n_obs_steps: int = 1 + chunk_size: int = 50 + n_action_steps: int = 50 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.IDENTITY, + "STATE": NormalizationMode.MEAN_STD, + "ACTION": NormalizationMode.MEAN_STD, + } + ) + + # Shorter state and action vectors will be padded + max_state_dim: int = 32 + max_action_dim: int = 32 + + # Image preprocessing + resize_imgs_with_padding: tuple[int, int] = (512, 512) + + # Add empty images. Used by smolvla_aloha_sim which adds the empty + # left and right wrist cameras in addition to the top camera. + empty_cameras: int = 0 + + # Converts the joint and gripper values from the standard Aloha space to + # the space used by the pi internal runtime which was used to train the base model. + adapt_to_pi_aloha: bool = False + + # Converts joint dimensions to deltas with respect to the current state before passing to the model. + # Gripper dimensions will remain in absolute values. + use_delta_joint_actions_aloha: bool = False + + # Tokenizer + tokenizer_max_length: int = 48 + + # Decoding + num_steps: int = 10 + + # Attention utils + use_cache: bool = True + + # Finetuning settings + freeze_vision_encoder: bool = True + train_expert_only: bool = True + train_state_proj: bool = True + + # Training presets + optimizer_lr: float = 1e-4 + optimizer_betas: tuple[float, float] = (0.9, 0.95) + optimizer_eps: float = 1e-8 + optimizer_weight_decay: float = 1e-10 + optimizer_grad_clip_norm: float = 10 + + scheduler_warmup_steps: int = 1_000 + scheduler_decay_steps: int = 30_000 + scheduler_decay_lr: float = 2.5e-6 + + vlm_model_name: str = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct" # Select the VLM backbone. + load_vlm_weights: bool = False # Set to True in case of training the expert from scratch. True when init from pretrained SmolVLA weights + + add_image_special_tokens: bool = False # Whether to use special image tokens around image features. + + attention_mode: str = "cross_attn" + + prefix_length: int = -1 + + pad_language_to: str = "longest" # "max_length" + + num_expert_layers: int = -1 # Less or equal to 0 is the default where the action expert has the same number of layers of VLM. Otherwise the expert have less layers. + num_vlm_layers: int = 16 # Number of layers used in the VLM (first num_vlm_layers layers) + self_attn_every_n_layers: int = 2 # Interleave SA layers each self_attn_every_n_layers + expert_width_multiplier: float = 0.75 # The action expert hidden size (wrt to the VLM) + + min_period: float = 4e-3 # sensitivity range for the timestep used in sine-cosine positional encoding + max_period: float = 4.0 + + def __post_init__(self): + super().__post_init__() + + """Input validation (not exhaustive).""" + if self.n_action_steps > self.chunk_size: + raise ValueError( + f"The chunk size is the upper bound for the number of action steps per model invocation. Got " + f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`." + ) + if self.use_delta_joint_actions_aloha: + raise NotImplementedError( + "`use_delta_joint_actions_aloha` is used by smolvla for aloha real models. It is not ported yet in LeRobot." + ) + + def validate_features(self) -> None: + for i in range(self.empty_cameras): + key = f"observation.images.empty_camera_{i}" + empty_camera = PolicyFeature( + type=FeatureType.VISUAL, + shape=(3, 480, 640), + ) + self.input_features[key] = empty_camera + + def get_optimizer_preset(self) -> AdamWConfig: + return AdamWConfig( + lr=self.optimizer_lr, + betas=self.optimizer_betas, + eps=self.optimizer_eps, + weight_decay=self.optimizer_weight_decay, + grad_clip_norm=self.optimizer_grad_clip_norm, + ) + + def get_scheduler_preset(self): + return CosineDecayWithWarmupSchedulerConfig( + peak_lr=self.optimizer_lr, + decay_lr=self.scheduler_decay_lr, + num_warmup_steps=self.scheduler_warmup_steps, + num_decay_steps=self.scheduler_decay_steps, + ) + + @property + def observation_delta_indices(self) -> list: + return [0] + + @property + def action_delta_indices(self) -> list: + return list(range(self.chunk_size)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/smolvla/modeling_smolvla.py b/lerobot/common/policies/smolvla/modeling_smolvla.py new file mode 100644 index 0000000000000000000000000000000000000000..afdb31abaa12f2775df0aa3f46fb4979fb60787a --- /dev/null +++ b/lerobot/common/policies/smolvla/modeling_smolvla.py @@ -0,0 +1,921 @@ +#!/usr/bin/env python + +# Copyright 2025 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +SmolVLA: + +[Paper](https://huggingface.co/papers/2506.01844) + +Designed by Hugging Face. + +Install smolvla extra dependencies: +```bash +pip install -e ".[smolvla]" +``` + +Example of finetuning the smolvla pretrained model (`smolvla_base`): +```bash +python lerobot/scripts/train.py \ +--policy.path=lerobot/smolvla_base \ +--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \ +--batch_size=64 \ +--steps=200000 +``` + +Example of finetuning a smolVLA. SmolVLA is composed of a pretrained VLM, +and an action expert. +```bash +python lerobot/scripts/train.py \ +--policy.type=smolvla \ +--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \ +--batch_size=64 \ +--steps=200000 +``` + +Example of using the smolvla pretrained model outside LeRobot training framework: +```python +policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base") +``` + +""" + +import math +import os +import re +from collections import deque + +import safetensors +import torch +import torch.nn.functional as F # noqa: N812 +from torch import Tensor, nn +from transformers import AutoProcessor + +from lerobot.common.constants import ACTION, OBS_STATE +from lerobot.common.policies.normalize import ( + Normalize, + Unnormalize, +) +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.smolvla.configuration_smolvla import SmolVLAConfig +from lerobot.common.policies.smolvla.smolvlm_with_expert import SmolVLMWithExpertModel +from lerobot.common.policies.utils import ( + populate_queues, +) +from lerobot.common.utils.utils import get_safe_dtype + +# Matches ".soNNN", optionally followed by "-something", up to the "_buffer_" marker +_VARIANT_RE = re.compile(r"\.so\d+(?:-[\w]+)?_buffer_") + + +def canonicalise(k: str) -> str: + """ + Remove dataset-variant markers like '.so100-blue_' or '.so100_' from a + normalisation-buffer key. + """ + return _VARIANT_RE.sub(".buffer_", k) + + +def standardise_state_dict( + checkpoint: dict[str, torch.Tensor], ref_keys: set[str], *, verbose: bool = True +) -> tuple[dict[str, torch.Tensor], list[str]]: + """ + • Re-keys `checkpoint ` so that every entry matches the *reference* key set. + • If several variant keys collapse to the same canonical name we keep the + first one and log the collision. + • Returns the new dict + a list of entries that could not be matched. + """ + out, collisions, unmatched = {}, {}, [] + + for k, v in checkpoint.items(): + canon = canonicalise(k) + if canon in ref_keys: + if canon in out: # duplicate after collapsing + collisions.setdefault(canon, []).append(k) + else: + out[canon] = v + else: + unmatched.append(k) + + if verbose: + for canon, variants in collisions.items(): + print(f"[standardise_state_dict] '{canon}' ← {variants}") + if unmatched: + print(f"[standardise_state_dict] kept {len(unmatched)} unmatched keys") + + out.update({k: checkpoint[k] for k in unmatched}) + return out, unmatched + + +def rename_checkpoint_keys(checkpoint: dict, rename_str: str): + """ + Renames keys in a checkpoint dictionary based on the given rename string. + + Args: + checkpoint (dict): The checkpoint dictionary. + rename_str (str): A string specifying key mappings in the format "old1//new1,old2//new2". + + Returns: + dict: The modified checkpoint with renamed keys. + """ + + rename_dict = dict(pair.split("//") for pair in rename_str.split(",")) + + new_checkpoint = {} + for k, v in checkpoint.items(): + for old_key, new_key in rename_dict.items(): + if old_key in k: + k = k.replace(old_key, new_key) + new_checkpoint[k] = v + return new_checkpoint + + +def load_smolvla( + model: torch.nn.Module, + filename: str | os.PathLike, + *, + device: str = "cpu", + checkpoint_keys_mapping: str = "", +) -> torch.nn.Module: + state_dict = safetensors.torch.load_file(filename, device=device) + + # Optional user-supplied renames (e.g. "model._orig_mod.//model.") + if checkpoint_keys_mapping and "//" in checkpoint_keys_mapping: + state_dict = rename_checkpoint_keys(state_dict, checkpoint_keys_mapping) + + state_dict, _ = standardise_state_dict(state_dict, set(model.state_dict().keys())) + + # HACK(aliberts): to not overwrite normalization parameters as they should come from the dataset + norm_keys = ("normalize_inputs", "normalize_targets", "unnormalize_outputs") + state_dict = {k: v for k, v in state_dict.items() if not k.startswith(norm_keys)} + + missing, unexpected = model.load_state_dict(state_dict, strict=False) + + if not all(key.startswith(norm_keys) for key in missing) or unexpected: + raise RuntimeError( + "SmolVLA %d missing / %d unexpected keys", + len(missing), + len(unexpected), + ) + + return model + + +def create_sinusoidal_pos_embedding( + time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu" +) -> Tensor: + """Computes sine-cosine positional embedding vectors for scalar positions.""" + if dimension % 2 != 0: + raise ValueError(f"dimension ({dimension}) must be divisible by 2") + + if time.ndim != 1: + raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.") + + dtype = get_safe_dtype(torch.float64, device.type) + fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device) + period = min_period * (max_period / min_period) ** fraction + + # Compute the outer product + scaling_factor = 1.0 / period * 2 * math.pi + sin_input = scaling_factor[None, :] * time[:, None] + pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1) + return pos_emb + + +def sample_beta(alpha, beta, bsize, device): + gamma1 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / alpha) + gamma2 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / beta) + return gamma1 / (gamma1 + gamma2) + + +def make_att_2d_masks(pad_masks, att_masks): + """Copied from big_vision. + + Tokens can attend to valid inputs tokens which have a cumulative mask_ar + smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to + setup several types of attention, for example: + + [[1 1 1 1 1 1]]: pure causal attention. + + [[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between + themselves and the last 3 tokens have a causal attention. The first + entry could also be a 1 without changing behaviour. + + [[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a + block can attend all previous blocks and all tokens on the same block. + + Args: + input_mask: bool[B, N] true if its part of the input, false if padding. + mask_ar: int32[B, N] mask that's 1 where previous tokens cannot depend on + it and 0 where it shares the same attention mask as the previous token. + """ + if att_masks.ndim != 2: + raise ValueError(att_masks.ndim) + if pad_masks.ndim != 2: + raise ValueError(pad_masks.ndim) + + cumsum = torch.cumsum(att_masks, dim=1) + att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None] + pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None] + att_2d_masks = att_2d_masks & pad_2d_masks + return att_2d_masks + + +def resize_with_pad(img, width, height, pad_value=-1): + # assume no-op when width height fits already + if img.ndim != 4: + raise ValueError(f"(b,c,h,w) expected, but {img.shape}") + + cur_height, cur_width = img.shape[2:] + + ratio = max(cur_width / width, cur_height / height) + resized_height = int(cur_height / ratio) + resized_width = int(cur_width / ratio) + resized_img = F.interpolate( + img, size=(resized_height, resized_width), mode="bilinear", align_corners=False + ) + + pad_height = max(0, int(height - resized_height)) + pad_width = max(0, int(width - resized_width)) + + # pad on left and top of image + padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value) + return padded_img + + +def pad_vector(vector, new_dim): + """Can be (batch_size x sequence_length x features_dimension) + or (batch_size x features_dimension) + """ + if vector.shape[-1] == new_dim: + return vector + shape = list(vector.shape) + current_dim = shape[-1] + shape[-1] = new_dim + new_vector = torch.zeros(*shape, dtype=vector.dtype, device=vector.device) + new_vector[..., :current_dim] = vector + return new_vector + + +def normalize(x, min_val, max_val): + return (x - min_val) / (max_val - min_val) + + +def unnormalize(x, min_val, max_val): + return x * (max_val - min_val) + min_val + + +def safe_arcsin(value): + # This ensures that the input stays within + # [−1,1] to avoid invalid values for arcsin + return torch.arcsin(torch.clamp(value, -1.0, 1.0)) + + +def aloha_gripper_to_angular(value): + # Aloha transforms the gripper positions into a linear space. The following code + # reverses this transformation to be consistent with smolvla which is pretrained in + # angular space. + # + # These values are coming from the Aloha code: + # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED + value = unnormalize(value, min_val=0.01844, max_val=0.05800) + + # This is the inverse of the angular to linear transformation inside the Interbotix code. + def linear_to_radian(linear_position, arm_length, horn_radius): + value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position) + return safe_arcsin(value) + + # The constants are taken from the Interbotix code. + value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022) + + # Normalize to [0, 1]. + # The values 0.4 and 1.5 were measured on an actual Trossen robot. + return normalize(value, min_val=0.4, max_val=1.5) + + +def aloha_gripper_from_angular(value): + # Convert from the gripper position used by smolvla to the gripper position that is used by Aloha. + # Note that the units are still angular but the range is different. + + # The values 0.4 and 1.5 were measured on an actual Trossen robot. + value = unnormalize(value, min_val=0.4, max_val=1.5) + + # These values are coming from the Aloha code: + # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE + return normalize(value, min_val=-0.6213, max_val=1.4910) + + +def aloha_gripper_from_angular_inv(value): + # Directly inverts the gripper_from_angular function. + value = unnormalize(value, min_val=-0.6213, max_val=1.4910) + return normalize(value, min_val=0.4, max_val=1.5) + + +class SmolVLAPolicy(PreTrainedPolicy): + """Wrapper class around VLAFlowMatching model to train and run inference within LeRobot.""" + + config_class = SmolVLAConfig + name = "smolvla" + + def __init__( + self, + config: SmolVLAConfig, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + + super().__init__(config) + config.validate_features() + self.config = config + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + self.language_tokenizer = AutoProcessor.from_pretrained(self.config.vlm_model_name).tokenizer + self.model = VLAFlowMatching(config) + self.reset() + + def reset(self): + """This should be called whenever the environment is reset.""" + self._queues = { + ACTION: deque(maxlen=self.config.n_action_steps), + } + + # HACK(aliberts, danaaubakirova): we overwrite this classmethod here to fix smolVLA-specific issues + @classmethod + def _load_as_safetensor( + cls, + model: "SmolVLAPolicy", + model_file: str, + map_location: str, + strict: bool, + ): + safetensors.torch.load_model(model, model_file, strict=strict, device=map_location) + return load_smolvla( + model, + model_file, + device=map_location, + checkpoint_keys_mapping="model._orig_mod.//model.", + ) + + def get_optim_params(self) -> dict: + return self.parameters() + + @torch.no_grad + def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor: + """Select a single action given environment observations. + + This method wraps `select_actions` in order to return one action at a time for execution in the + environment. It works by managing the actions in a queue and only calling `select_actions` when the + queue is empty. + """ + self.eval() + + if self.config.adapt_to_pi_aloha: + batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + + batch = self.normalize_inputs(batch) + + self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION]) + # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by + # querying the policy. + if len(self._queues[ACTION]) == 0: + for k in batch: + if k in self._queues: + batch[k] = torch.stack(list(self._queues[k]), dim=1) + images, img_masks = self.prepare_images(batch) + state = self.prepare_state(batch) + lang_tokens, lang_masks = self.prepare_language(batch) + + actions = self.model.sample_actions( + images, img_masks, lang_tokens, lang_masks, state, noise=noise + ) + # Unpad actions + original_action_dim = self.config.action_feature.shape[0] + actions = actions[:, :, :original_action_dim] + + actions = self.unnormalize_outputs({"action": actions})["action"] + + if self.config.adapt_to_pi_aloha: + actions = self._pi_aloha_encode_actions(actions) + + # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue + # effectively has shape (n_action_steps, batch_size, *), hence the transpose. + self._queues[ACTION].extend(actions.transpose(0, 1)[: self.config.n_action_steps]) + return self._queues[ACTION].popleft() + + def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> dict[str, Tensor]: + """Do a full training forward pass to compute the loss""" + if self.config.adapt_to_pi_aloha: + batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION]) + batch = self.normalize_inputs(batch) + batch = self.normalize_targets(batch) + images, img_masks = self.prepare_images(batch) + state = self.prepare_state(batch) + lang_tokens, lang_masks = self.prepare_language(batch) + actions = self.prepare_action(batch) + actions_is_pad = batch.get("actions_id_pad") + loss_dict = {} + losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time) + loss_dict["losses_after_forward"] = losses.clone() + + if actions_is_pad is not None: + in_episode_bound = ~actions_is_pad + losses = losses * in_episode_bound.unsqueeze(-1) + loss_dict["losses_after_in_ep_bound"] = losses.clone() + + # Remove padding + losses = losses[:, :, : self.config.max_action_dim] + loss_dict["losses_after_rm_padding"] = losses.clone() + + # For backward pass + loss = losses.mean() + # For backward pass + loss_dict["loss"] = loss.item() + return loss, loss_dict + + def prepare_images(self, batch): + """Apply SmolVLA preprocessing to the images, like resizing to 224x224 and padding to keep aspect ratio, and + convert pixel range from [0.0, 1.0] to [-1.0, 1.0] as requested by SigLIP. + """ + images = [] + img_masks = [] + present_img_keys = [key for key in self.config.image_features if key in batch] + missing_img_keys = [key for key in self.config.image_features if key not in batch] + + if len(present_img_keys) == 0: + raise ValueError( + f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})" + ) + # Preprocess image features present in the batch + for key in present_img_keys: + img = batch[key][:, -1, :, :, :] if batch[key].ndim == 5 else batch[key] + if self.config.resize_imgs_with_padding is not None: + img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0) + + # Normalize from range [0,1] to [-1,1] as expacted by siglip + img = img * 2.0 - 1.0 + + bsize = img.shape[0] + device = img.device + if f"{key}_padding_mask" in batch: + mask = batch[f"{key}_padding_mask"].bool() + else: + mask = torch.ones(bsize, dtype=torch.bool, device=device) + images.append(img) + img_masks.append(mask) + + # Create image features not present in the batch + # as fully 0 padded images. + for num_empty_cameras in range(len(missing_img_keys)): + if num_empty_cameras >= self.config.empty_cameras: + break + img = torch.ones_like(img) * -1 + mask = torch.zeros_like(mask) + images.append(img) + img_masks.append(mask) + return images, img_masks + + def prepare_language(self, batch) -> tuple[Tensor, Tensor]: + """Tokenize the text input""" + device = batch[OBS_STATE].device + tasks = batch["task"] + if isinstance(tasks, str): + tasks = [tasks] + + if len(tasks) == 1: + tasks = [tasks[0] for _ in range(batch[OBS_STATE].shape[0])] + + tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks] + + tokenized_prompt = self.language_tokenizer.__call__( + tasks, + padding=self.config.pad_language_to, + padding_side="right", + max_length=self.config.tokenizer_max_length, + return_tensors="pt", + ) + lang_tokens = tokenized_prompt["input_ids"].to(device=device) + lang_masks = tokenized_prompt["attention_mask"].to(device=device, dtype=torch.bool) + + return lang_tokens, lang_masks + + def _pi_aloha_decode_state(self, state): + # Flip the joints. + for motor_idx in [1, 2, 8, 9]: + state[:, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx]) + return state + + def _pi_aloha_encode_actions(self, actions): + # Flip the joints. + for motor_idx in [1, 2, 8, 9]: + actions[:, :, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx]) + return actions + + def _pi_aloha_encode_actions_inv(self, actions): + # Flip the joints again. + for motor_idx in [1, 2, 8, 9]: + actions[:, :, motor_idx] *= -1 + # Reverse the gripper transformation that is being applied by the Aloha runtime. + for motor_idx in [6, 13]: + actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx]) + return actions + + def prepare_state(self, batch): + """Pad state""" + state = batch[OBS_STATE][:, -1, :] if batch[OBS_STATE].ndim > 2 else batch[OBS_STATE] + state = pad_vector(state, self.config.max_state_dim) + return state + + def prepare_action(self, batch): + """Pad action""" + actions = pad_vector(batch[ACTION], self.config.max_action_dim) + return actions + + +def pad_tensor(tensor, max_len, pad_value=0): + """ + Efficiently pads a tensor along sequence dimension to match max_len. + + Args: + tensor (torch.Tensor): Shape (B, L, ...) or (B, L). + max_len (int): Fixed sequence length. + pad_value (int/float): Value for padding. + + Returns: + torch.Tensor: Shape (B, max_len, ...) or (B, max_len). + """ + b, d = tensor.shape[:2] + + # Create a padded tensor of max_len and copy the existing values + padded_tensor = torch.full( + (b, max_len, *tensor.shape[2:]), pad_value, dtype=tensor.dtype, device=tensor.device + ) + padded_tensor[:, :d] = tensor # Efficient in-place copy + + return padded_tensor + + +class VLAFlowMatching(nn.Module): + """ + SmolVLA + + [Paper]() + + Designed by Hugging Face. + ┌──────────────────────────────┐ + │ actions │ + │ ▲ │ + │ ┌─────────┐ ┌─|────┐ │ + │ | │────► │ │ │ + │ | │ kv │ │ │ + │ | │────► │Action│ │ + │ | VLM │cache │Expert│ | + │ │ │────► | │ │ + │ │ │ │ │ │ + │ └▲──▲───▲─┘ └───▲──┘ | + │ │ | | │ | + │ | | | noise │ + │ │ │ state │ + │ │ language tokens │ + │ image(s) │ + └──────────────────────────────┘ + """ + + def __init__(self, config): + super().__init__() + self.config = config + + self.vlm_with_expert = SmolVLMWithExpertModel( + model_id=self.config.vlm_model_name, + freeze_vision_encoder=self.config.freeze_vision_encoder, + train_expert_only=self.config.train_expert_only, + load_vlm_weights=self.config.load_vlm_weights, + attention_mode=self.config.attention_mode, + num_expert_layers=self.config.num_expert_layers, + num_vlm_layers=self.config.num_vlm_layers, + self_attn_every_n_layers=self.config.self_attn_every_n_layers, + expert_width_multiplier=self.config.expert_width_multiplier, + ) + self.state_proj = nn.Linear( + self.config.max_state_dim, self.vlm_with_expert.config.text_config.hidden_size + ) + self.action_in_proj = nn.Linear(self.config.max_action_dim, self.vlm_with_expert.expert_hidden_size) + self.action_out_proj = nn.Linear(self.vlm_with_expert.expert_hidden_size, self.config.max_action_dim) + + self.action_time_mlp_in = nn.Linear( + self.vlm_with_expert.expert_hidden_size * 2, self.vlm_with_expert.expert_hidden_size + ) + self.action_time_mlp_out = nn.Linear( + self.vlm_with_expert.expert_hidden_size, self.vlm_with_expert.expert_hidden_size + ) + + self.set_requires_grad() + self.fake_image_token = self.vlm_with_expert.processor.tokenizer.fake_image_token_id + self.global_image_token = self.vlm_with_expert.processor.tokenizer.global_image_token_id + self.global_image_start_token = torch.tensor( + [self.fake_image_token, self.global_image_token], dtype=torch.long + ) + + self.add_image_special_tokens = self.config.add_image_special_tokens + self.image_end_token = torch.tensor([self.fake_image_token], dtype=torch.long) + self.prefix_length = self.config.prefix_length + + def set_requires_grad(self): + for params in self.state_proj.parameters(): + params.requires_grad = self.config.train_state_proj + + def sample_noise(self, shape, device): + noise = torch.normal( + mean=0.0, + std=1.0, + size=shape, + dtype=torch.float32, + device=device, + ) + return noise + + def sample_time(self, bsize, device): + time_beta = sample_beta(1.5, 1.0, bsize, device) + time = time_beta * 0.999 + 0.001 + return time.to(dtype=torch.float32, device=device) + + def embed_prefix( + self, images, img_masks, lang_tokens, lang_masks, state: torch.Tensor = None + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Embed images with SigLIP and language tokens with embedding layer to prepare + for SmolVLM transformer processing. + """ + embs = [] + pad_masks = [] + att_masks = [] + for _img_idx, ( + img, + img_mask, + ) in enumerate(zip(images, img_masks, strict=False)): + if self.add_image_special_tokens: + image_start_token = ( + self.vlm_with_expert.embed_language_tokens( + self.global_image_start_token.to(device=self.vlm_with_expert.vlm.device) + ) + .unsqueeze(0) + .expand(img.shape[0], -1, -1) + ) + image_start_mask = torch.ones_like( + image_start_token[:, :, 0], dtype=torch.bool, device=image_start_token.device + ) + att_masks += [0] * (image_start_mask.shape[-1]) + embs.append(image_start_token) + pad_masks.append(image_start_mask) + + img_emb = self.vlm_with_expert.embed_image(img) + img_emb = img_emb + + # Normalize image embeddings + img_emb_dim = img_emb.shape[-1] + img_emb = img_emb * torch.tensor(img_emb_dim**0.5, dtype=img_emb.dtype, device=img_emb.device) + + bsize, num_img_embs = img_emb.shape[:2] + img_mask = img_mask[:, None].expand(bsize, num_img_embs) + + embs.append(img_emb) + pad_masks.append(img_mask) + + att_masks += [0] * (num_img_embs) + if self.add_image_special_tokens: + image_end_token = ( + self.vlm_with_expert.embed_language_tokens( + self.image_end_token.to(device=self.vlm_with_expert.vlm.device) + ) + .unsqueeze(0) + .expand(img.shape[0], -1, -1) + ) + image_end_mask = torch.ones_like( + image_end_token[:, :, 0], dtype=torch.bool, device=image_end_token.device + ) + embs.append(image_end_token) + pad_masks.append(image_end_mask) + att_masks += [0] * (image_end_mask.shape[1]) + lang_emb = self.vlm_with_expert.embed_language_tokens(lang_tokens) + # Normalize language embeddings + lang_emb_dim = lang_emb.shape[-1] + lang_emb = lang_emb * math.sqrt(lang_emb_dim) + + embs.append(lang_emb) + pad_masks.append(lang_masks) + + num_lang_embs = lang_emb.shape[1] + att_masks += [0] * num_lang_embs + + state_emb = self.state_proj(state) + state_emb = state_emb[:, None, :] if state_emb.ndim == 2 else state_emb + embs.append(state_emb) + bsize = state_emb.shape[0] + device = state_emb.device + + states_seq_len = state_emb.shape[1] + state_mask = torch.ones(bsize, states_seq_len, dtype=torch.bool, device=device) + pad_masks.append(state_mask) + + # Set attention masks so that image and language inputs do not attend to state or actions + att_masks += [1] * (states_seq_len) + embs = torch.cat(embs, dim=1) + pad_masks = torch.cat(pad_masks, dim=1) + att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device) + att_masks = att_masks[None, :] + + seq_len = pad_masks.shape[1] + if seq_len < self.prefix_length: + embs = pad_tensor(embs, self.prefix_length, pad_value=0) + pad_masks = pad_tensor(pad_masks, self.prefix_length, pad_value=0) + att_masks = pad_tensor(att_masks, self.prefix_length, pad_value=0) + + att_masks = att_masks.expand(bsize, -1) + + return embs, pad_masks, att_masks + + def embed_suffix(self, noisy_actions, timestep): + """Embed state, noisy_actions, timestep to prepare for Expert Gemma processing.""" + embs = [] + pad_masks = [] + att_masks = [] + + # Fuse timestep + action information using an MLP + action_emb = self.action_in_proj(noisy_actions) + device = action_emb.device + bsize = action_emb.shape[0] + dtype = action_emb.dtype + # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1] + time_emb = create_sinusoidal_pos_embedding( + timestep, + self.vlm_with_expert.expert_hidden_size, + self.config.min_period, + self.config.max_period, + device=device, + ) + time_emb = time_emb.type(dtype=dtype) + + time_emb = time_emb[:, None, :].expand_as(action_emb) + action_time_emb = torch.cat([action_emb, time_emb], dim=2) + + action_time_emb = self.action_time_mlp_in(action_time_emb) + action_time_emb = F.silu(action_time_emb) # swish == silu + action_time_emb = self.action_time_mlp_out(action_time_emb) + + # Add to input tokens + embs.append(action_time_emb) + + bsize, action_time_dim = action_time_emb.shape[:2] + action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=device) + pad_masks.append(action_time_mask) + + # Set attention masks so that image, language and state inputs do not attend to action tokens + att_masks += [1] * self.config.chunk_size + embs = torch.cat(embs, dim=1) + pad_masks = torch.cat(pad_masks, dim=1) + att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device) + att_masks = att_masks[None, :].expand(bsize, len(att_masks)) + return embs, pad_masks, att_masks + + def forward( + self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None + ) -> Tensor: + """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)""" + if noise is None: + noise = self.sample_noise(actions.shape, actions.device) + + if time is None: + time = self.sample_time(actions.shape[0], actions.device) + + time_expanded = time[:, None, None] + x_t = time_expanded * noise + (1 - time_expanded) * actions + u_t = noise - actions + prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix( + images, img_masks, lang_tokens, lang_masks, state=state + ) + suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(x_t, time) + + pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1) + att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1) + + att_2d_masks = make_att_2d_masks(pad_masks, att_masks) + position_ids = torch.cumsum(pad_masks, dim=1) - 1 + (_, suffix_out), _ = self.vlm_with_expert.forward( + attention_mask=att_2d_masks, + position_ids=position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, suffix_embs], + use_cache=False, + fill_kv_cache=False, + ) + suffix_out = suffix_out[:, -self.config.chunk_size :] + # Original openpi code, upcast attention output + suffix_out = suffix_out.to(dtype=torch.float32) + v_t = self.action_out_proj(suffix_out) + losses = F.mse_loss(u_t, v_t, reduction="none") + return losses + + def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, noise=None) -> Tensor: + """Do a full inference forward and compute the action (batch_size x num_steps x num_motors)""" + bsize = state.shape[0] + device = state.device + + if noise is None: + actions_shape = (bsize, self.config.chunk_size, self.config.max_action_dim) + noise = self.sample_noise(actions_shape, device) + + prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix( + images, img_masks, lang_tokens, lang_masks, state=state + ) + prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks) + prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1 + # Compute image and language key value cache + _, past_key_values = self.vlm_with_expert.forward( + attention_mask=prefix_att_2d_masks, + position_ids=prefix_position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, None], + use_cache=self.config.use_cache, + fill_kv_cache=True, + ) + dt = -1.0 / self.config.num_steps + dt = torch.tensor(dt, dtype=torch.float32, device=device) + + x_t = noise + time = torch.tensor(1.0, dtype=torch.float32, device=device) + while time >= -dt / 2: + expanded_time = time.expand(bsize) + v_t = self.denoise_step( + prefix_pad_masks, + past_key_values, + x_t, + expanded_time, + ) + # Euler step + x_t += dt * v_t + time += dt + return x_t + + def denoise_step( + self, + prefix_pad_masks, + past_key_values, + x_t, + timestep, + ): + """Apply one denoising step of the noise `x_t` at a given timestep.""" + suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(x_t, timestep) + + suffix_len = suffix_pad_masks.shape[1] + batch_size = prefix_pad_masks.shape[0] + prefix_len = prefix_pad_masks.shape[1] + prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len) + + suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks) + + full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2) + prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None] + position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1 + + outputs_embeds, _ = self.vlm_with_expert.forward( + attention_mask=full_att_2d_masks, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=[None, suffix_embs], + use_cache=self.config.use_cache, + fill_kv_cache=False, + ) + suffix_out = outputs_embeds[1] + suffix_out = suffix_out[:, -self.config.chunk_size :] + suffix_out = suffix_out.to(dtype=torch.float32) + v_t = self.action_out_proj(suffix_out) + return v_t diff --git a/lerobot/common/policies/smolvla/smolvlm_with_expert.py b/lerobot/common/policies/smolvla/smolvlm_with_expert.py new file mode 100644 index 0000000000000000000000000000000000000000..73282bfb91ff29bb08ea3c90e21a2be6a2ad83dd --- /dev/null +++ b/lerobot/common/policies/smolvla/smolvlm_with_expert.py @@ -0,0 +1,550 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +from typing import List, Optional + +import torch +from torch import nn +from transformers import ( + AutoConfig, + AutoModel, + AutoModelForImageTextToText, + AutoProcessor, + SmolVLMForConditionalGeneration, +) + + +def apply_rope(x, positions, max_wavelength=10_000): + """ + Applies RoPE positions [B, L] to x [B, L, H, D]. + """ + d_half = x.shape[-1] // 2 + device = x.device + dtype = x.dtype + x = x.to(torch.float32) + + freq_exponents = (2.0 / x.shape[-1]) * torch.arange(d_half, dtype=torch.float32, device=device) + timescale = max_wavelength**freq_exponents + radians = positions[..., None].to(torch.float32) / timescale[None, None, :].to(torch.float32) + + radians = radians[..., None, :] + + sin = torch.sin(radians) # .to(dtype=dtype) + cos = torch.cos(radians) # .to(dtype=dtype) + + x1, x2 = x.split(d_half, dim=-1) + res = torch.empty_like(x) + res[..., :d_half] = x1 * cos - x2 * sin + res[..., d_half:] = x2 * cos + x1 * sin + + return res.to(dtype) + + +def get_intermediate_size(hidden_dim, ffn_dim_multiplier=4, multiple_of=256): + hidden_dim = int(2 * hidden_dim / 3) + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + return hidden_dim + + +class SmolVLMWithExpertModel(nn.Module): + def __init__( + self, + model_id: str = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct", + load_vlm_weights: bool = True, + train_expert_only: bool = True, + freeze_vision_encoder: bool = False, + attention_mode: str = "self_attn", + num_expert_layers: int = -1, + num_vlm_layers: int = -1, + self_attn_every_n_layers: int = -1, + expert_width_multiplier: float = 0.5, + ): + super().__init__() + if load_vlm_weights: + print(f"Loading {model_id} weights ...") + self.vlm = AutoModelForImageTextToText.from_pretrained( + model_id, + device_map="auto", + torch_dtype="bfloat16", + low_cpu_mem_usage=True, + ) + config = self.vlm.config + else: + config = AutoConfig.from_pretrained(model_id) + self.vlm = SmolVLMForConditionalGeneration(config=config) + self.processor = AutoProcessor.from_pretrained(model_id) + if num_vlm_layers > 0: + print(f"Reducing the number of VLM layers to {num_vlm_layers} ...") + self.get_vlm_model().text_model.layers = self.get_vlm_model().text_model.layers[:num_vlm_layers] + self.num_vlm_layers = len(self.get_vlm_model().text_model.layers) + self.config = config + # Smaller lm expert + lm_expert_config = copy.deepcopy(config.text_config) + hidden_size = lm_expert_config.hidden_size + lm_expert_config.hidden_size = int(hidden_size * expert_width_multiplier) # hidden_size // 2 + lm_expert_config.intermediate_size = get_intermediate_size(int(hidden_size * expert_width_multiplier)) + lm_expert_config.num_hidden_layers = self.num_vlm_layers + if num_expert_layers > 0: + assert len(self.get_vlm_model().text_model.layers) % num_expert_layers == 0, ( + f"Number of layers in the VLM {len(self.get_vlm_model().text_model.layers)} are not multiple of num_expert_layers {num_expert_layers}" + ) + lm_expert_config.num_hidden_layers = num_expert_layers + self.lm_expert = AutoModel.from_config(lm_expert_config) + + self.num_expert_layers = len(self.lm_expert.layers) + self.self_attn_every_n_layers = self_attn_every_n_layers + if "cross" in attention_mode: + # Reshape qkv projections to have the same input dimension as the vlm + for layer_idx in range(len(self.lm_expert.layers)): + if self.self_attn_every_n_layers > 0 and layer_idx % self.self_attn_every_n_layers == 0: + continue + self.lm_expert.layers[layer_idx].self_attn.k_proj = nn.Linear( + config.text_config.num_key_value_heads * config.text_config.head_dim, + lm_expert_config.num_key_value_heads * lm_expert_config.head_dim, + bias=lm_expert_config.attention_bias, + ) + self.lm_expert.layers[layer_idx].self_attn.v_proj = nn.Linear( + config.text_config.num_key_value_heads * config.text_config.head_dim, + lm_expert_config.num_key_value_heads * lm_expert_config.head_dim, + bias=lm_expert_config.attention_bias, + ) + # Remove unused embed_tokens + self.lm_expert.embed_tokens = None + + self.num_attention_heads = self.config.text_config.num_attention_heads + self.num_key_value_heads = self.config.text_config.num_key_value_heads + + self.freeze_vision_encoder = freeze_vision_encoder + self.train_expert_only = train_expert_only + self.attention_mode = attention_mode + self.expert_hidden_size = lm_expert_config.hidden_size + self.set_requires_grad() + + def get_vlm_model(self): + return self.vlm.model + + def set_requires_grad(self): + if self.freeze_vision_encoder: + self.get_vlm_model().vision_model.eval() + for params in self.get_vlm_model().vision_model.parameters(): + params.requires_grad = False + if self.train_expert_only: + self.vlm.eval() + for params in self.vlm.parameters(): + params.requires_grad = False + else: + # To avoid unused params issue with distributed training + last_layers = [self.num_vlm_layers - 1] + if ( + self.num_vlm_layers != self.num_expert_layers + and self.num_vlm_layers % self.num_expert_layers == 0 + ): + last_layers.append(self.num_vlm_layers - 2) + frozen_layers = [ + "lm_head", + "text_model.model.norm.weight", + ] + for layer in last_layers: + frozen_layers.append(f"text_model.model.layers.{layer}.") + + for name, params in self.vlm.named_parameters(): + if any(k in name for k in frozen_layers): + params.requires_grad = False + # To avoid unused params issue with distributed training + for name, params in self.lm_expert.named_parameters(): + if "lm_head" in name: + params.requires_grad = False + + def train(self, mode: bool = True): + super().train(mode) + + if self.freeze_vision_encoder: + self.get_vlm_model().vision_model.eval() + + if self.train_expert_only: + self.vlm.eval() + + def embed_image(self, image: torch.Tensor): + patch_attention_mask = None + # Get sequence from the vision encoder + image_hidden_states = ( + self.get_vlm_model() + .vision_model( + pixel_values=image.to(dtype=self.get_vlm_model().vision_model.dtype), + patch_attention_mask=patch_attention_mask, + ) + .last_hidden_state + ) + # Modality projection & resampling + image_hidden_states = self.get_vlm_model().connector(image_hidden_states) + return image_hidden_states + + def embed_language_tokens(self, tokens: torch.Tensor): + return self.get_vlm_model().text_model.get_input_embeddings()(tokens) + + def forward_attn_layer( + self, + model_layers, + inputs_embeds, + layer_idx, + position_ids, + attention_mask, + batch_size, + head_dim, + use_cache: bool = True, + fill_kv_cache: bool = True, + past_key_values=None, + ) -> list[torch.Tensor]: + query_states = [] + key_states = [] + value_states = [] + for i, hidden_states in enumerate(inputs_embeds): + layer = model_layers[i][layer_idx] + if hidden_states is None or layer is None: + continue + hidden_states = layer.input_layernorm(hidden_states) + + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, layer.self_attn.head_dim) + + hidden_states = hidden_states.to(dtype=layer.self_attn.q_proj.weight.dtype) + query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape) + key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape) + value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape) + + query_states.append(query_state) + key_states.append(key_state) + value_states.append(value_state) + + # B,L,H,D with L sequence length, H number of heads, D head dim + # concatenate on the number of embeddings/tokens + query_states = torch.cat(query_states, dim=1) + key_states = torch.cat(key_states, dim=1) + value_states = torch.cat(value_states, dim=1) + seq_len = query_states.shape[1] + if seq_len < position_ids.shape[1]: + _position_ids = position_ids[:, :seq_len] + _attention_mask = attention_mask[:, :seq_len, :seq_len] + else: + _position_ids = position_ids + _attention_mask = attention_mask + + attention_mask_ = _attention_mask + position_ids_ = _position_ids + + query_states = apply_rope(query_states, position_ids_) + key_states = apply_rope(key_states, position_ids_) + + if use_cache and past_key_values is None: + past_key_values = {} + + if use_cache: + if fill_kv_cache: + past_key_values[layer_idx] = { + "key_states": key_states, + "value_states": value_states, + } + else: + # TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before. + # so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach + # the max len, then we (for instance) double the cache size. This implementation already exists + # in `transformers`. (molbap) + key_states = torch.cat([past_key_values[layer_idx]["key_states"], key_states], dim=1) + value_states = torch.cat([past_key_values[layer_idx]["value_states"], value_states], dim=1) + + attention_interface = self.get_attention_interface() + + att_output = attention_interface( + attention_mask_, batch_size, head_dim, query_states, key_states, value_states + ) + return [att_output], past_key_values + + def forward_cross_attn_layer( + self, + model_layers, + inputs_embeds, + layer_idx, + position_ids, + attention_mask, + batch_size, + head_dim, + use_cache: bool = True, + fill_kv_cache: bool = True, + past_key_values=None, + ) -> list[torch.Tensor]: + attention_interface = self.get_attention_interface() + + att_outputs = [] + assert len(inputs_embeds) == 2 or (use_cache and past_key_values is not None and not fill_kv_cache), ( + f"Both len(inputs_embeds) == {len(inputs_embeds)} and past_key_values is {past_key_values}" + ) + + if len(inputs_embeds) == 2 and not past_key_values: + # Prefix attention + seq_len = inputs_embeds[0].shape[1] + position_id, expert_position_id = position_ids[:, :seq_len], position_ids[:, seq_len:] + prefix_attention_mask = attention_mask[:, :seq_len, :seq_len] + + layer = model_layers[0][layer_idx] + + hidden_states = layer.input_layernorm(inputs_embeds[0]) + + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, layer.self_attn.head_dim) + + hidden_states = hidden_states.to(dtype=layer.self_attn.q_proj.weight.dtype) + query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape) + key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape) + value_states = layer.self_attn.v_proj(hidden_states).view(hidden_shape) + + # B,L,H,D with L sequence length, H number of heads, D head dim + query_states = apply_rope(query_state, position_id) + key_states = apply_rope(key_state, position_id) + + att_output = attention_interface( + prefix_attention_mask, batch_size, head_dim, query_states, key_states, value_states + ) + att_outputs.append(att_output) + else: + expert_position_id = position_ids + + if use_cache and past_key_values is None: + past_key_values = {} + + if use_cache: + if fill_kv_cache: + past_key_values[layer_idx] = { + "key_states": key_states, + "value_states": value_states, + } + else: + # TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before. + # so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach + # the max len, then we (for instance) double the cache size. This implementation already exists + # in `transformers`. (molbap) + key_states = past_key_values[layer_idx]["key_states"] + value_states = past_key_values[layer_idx]["value_states"] + + # Expert + expert_layer = model_layers[1][layer_idx] + if expert_layer is not None: + expert_hidden_states = expert_layer.input_layernorm(inputs_embeds[1]) + + expert_input_shape = expert_hidden_states.shape[:-1] + expert_hidden_shape = (*expert_input_shape, -1, expert_layer.self_attn.head_dim) + + expert_hidden_states = expert_hidden_states.to(dtype=expert_layer.self_attn.q_proj.weight.dtype) + expert_query_state = expert_layer.self_attn.q_proj(expert_hidden_states).view(expert_hidden_shape) + + _key_states = key_states.to(dtype=expert_layer.self_attn.k_proj.weight.dtype).view( + *key_states.shape[:2], -1 + ) + expert_key_states = expert_layer.self_attn.k_proj(_key_states).view( + *_key_states.shape[:-1], -1, expert_layer.self_attn.head_dim + ) # k_proj should have same dim as kv + + _value_states = value_states.to(dtype=expert_layer.self_attn.v_proj.weight.dtype).view( + *value_states.shape[:2], -1 + ) + expert_value_states = expert_layer.self_attn.v_proj(_value_states).view( + *_value_states.shape[:-1], -1, expert_layer.self_attn.head_dim + ) + + expert_position_id = ( + expert_position_id - torch.min(expert_position_id, dim=1, keepdim=True).values + ) # start from 0 + expert_attention_mask = attention_mask[ + :, -inputs_embeds[1].shape[1] :, : expert_key_states.shape[1] : + ] # take into account kv + + expert_query_states = apply_rope(expert_query_state, expert_position_id) + + att_output = attention_interface( + expert_attention_mask, + batch_size, + head_dim, + expert_query_states, + expert_key_states, + expert_value_states, + ) + att_outputs.append(att_output) + else: + att_outputs.append(None) + + # att_output = att_output.to(dtype=models[i].dtype) + return att_outputs, past_key_values + + def get_model_layers(self, models: list) -> list: + vlm_layers = [] + expert_layers = [] + multiple_of = self.num_vlm_layers // self.num_expert_layers + for i in range(self.num_vlm_layers): + if multiple_of > 0 and i > 0 and i % multiple_of != 0: + expert_layer = None + else: + expert_layer_index = i // multiple_of if multiple_of > 0 else i + expert_layer = models[1].layers[expert_layer_index] + vlm_layers.append(models[0].layers[i]) + expert_layers.append(expert_layer) + return [vlm_layers, expert_layers] + + def forward( + self, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: List[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + fill_kv_cache: Optional[bool] = None, + ): + models = [self.get_vlm_model().text_model, self.lm_expert] + model_layers = self.get_model_layers(models) + for hidden_states in inputs_embeds: + # TODO this is very inefficient + # dtype is always the same, batch size too (if > 1 len) + # device could be trickier in multi gpu edge cases but that's it + if hidden_states is None: + continue + batch_size = hidden_states.shape[0] + + # RMSNorm + num_layers = self.num_vlm_layers + head_dim = self.vlm.config.text_config.head_dim + for layer_idx in range(num_layers): + if ( + fill_kv_cache + or "cross" not in self.attention_mode + or (self.self_attn_every_n_layers > 0 and layer_idx % self.self_attn_every_n_layers == 0) + ): + att_outputs, past_key_values = self.forward_attn_layer( + model_layers, + inputs_embeds, + layer_idx, + position_ids, + attention_mask, + batch_size, + head_dim, + use_cache=use_cache, + fill_kv_cache=fill_kv_cache, + past_key_values=past_key_values, + ) + else: + att_outputs, past_key_values = self.forward_cross_attn_layer( + model_layers, + inputs_embeds, + layer_idx, + position_ids, + attention_mask, + batch_size, + head_dim, + use_cache=use_cache, + fill_kv_cache=fill_kv_cache, + past_key_values=past_key_values, + ) + outputs_embeds = [] + start = 0 + for i, hidden_states in enumerate(inputs_embeds): + layer = model_layers[i][layer_idx] + att_output = ( + att_outputs[i] if i < len(att_outputs) else att_outputs[0] + ) # in case of self_attn + if hidden_states is not None: + if layer is None: + outputs_embeds.append(hidden_states) + continue + end = start + hidden_states.shape[1] + + if att_output.dtype != layer.self_attn.o_proj.weight.dtype: + att_output = att_output.to(layer.self_attn.o_proj.weight.dtype) + att_out = att_output[:, start:end] + out_emb = layer.self_attn.o_proj(att_out) + + out_emb += hidden_states + after_first_residual = out_emb.clone() + + out_emb = layer.post_attention_layernorm(out_emb) + out_emb = layer.mlp(out_emb) + + out_emb += after_first_residual + + outputs_embeds.append(out_emb) + + start = end if len(att_outputs) == 1 else 0 + else: + outputs_embeds.append(None) + + inputs_embeds = outputs_embeds + + # final norm + outputs_embeds = [] + for i, hidden_states in enumerate(inputs_embeds): + if hidden_states is not None: + out_emb = models[i].norm(hidden_states) + outputs_embeds.append(out_emb) + else: + outputs_embeds.append(None) + return outputs_embeds, past_key_values + + def get_attention_interface(self): + attention_interface = self.eager_attention_forward + return attention_interface + + def eager_attention_forward( + self, attention_mask, batch_size, head_dim, query_states, key_states, value_states + ): + num_att_heads = self.num_attention_heads + num_key_value_heads = self.num_key_value_heads + num_key_value_groups = num_att_heads // num_key_value_heads + + sequence_length = key_states.shape[1] + + key_states = key_states[:, :, :, None, :].expand( + batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim + ) + key_states = key_states.reshape( + batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim + ) + + value_states = value_states[:, :, :, None, :].expand( + batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim + ) + value_states = value_states.reshape( + batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim + ) + + # Attention here is upcasted to float32 to match the original eager implementation. + query_states = query_states.to(dtype=torch.float32) + key_states = key_states.to(dtype=torch.float32) + + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + + att_weights = torch.matmul(query_states, key_states.transpose(2, 3)) + att_weights *= head_dim**-0.5 + + att_weights = att_weights.to(dtype=torch.float32) + big_neg = torch.finfo(att_weights.dtype).min # -2.3819763e38 # See gemma/modules.py + masked_att_weights = torch.where(attention_mask[:, None, :, :], att_weights, big_neg) + probs = nn.functional.softmax(masked_att_weights, dim=-1) + probs = probs.to(dtype=value_states.dtype) + + att_output = torch.matmul(probs, value_states.permute(0, 2, 1, 3)) + + att_output = att_output.permute(0, 2, 1, 3) + # we use -1 because sequence length can change + att_output = att_output.reshape(batch_size, -1, num_key_value_heads * num_key_value_groups * head_dim) + + return att_output diff --git a/lerobot/common/policies/tdmpc/configuration_tdmpc.py b/lerobot/common/policies/tdmpc/configuration_tdmpc.py new file mode 100644 index 0000000000000000000000000000000000000000..5b5cdb106e1b42b802749a0a007430ac76ed3137 --- /dev/null +++ b/lerobot/common/policies/tdmpc/configuration_tdmpc.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python + +# Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su, +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import NormalizationMode + + +@PreTrainedConfig.register_subclass("tdmpc") +@dataclass +class TDMPCConfig(PreTrainedConfig): + """Configuration class for TDMPCPolicy. + + Defaults are configured for training with xarm_lift_medium_replay providing proprioceptive and single + camera observations. + + The parameters you will most likely need to change are the ones which depend on the environment / sensors. + Those are: `input_shapes`, `output_shapes`, and perhaps `max_random_shift_ratio`. + + Args: + n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google + action repeats in Q-learning or ask your favorite chatbot) + horizon: Horizon for model predictive control. + n_action_steps: Number of action steps to take from the plan given by model predictive control. This + is an alternative to using action repeats. If this is set to more than 1, then we require + `n_action_repeats == 1`, `use_mpc == True` and `n_action_steps <= horizon`. Note that this + approach of using multiple steps from the plan is not in the original implementation. + input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents + the input data name, and the value is a list indicating the dimensions of the corresponding data. + For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], + indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't + include batch dimension or temporal dimension. + output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents + the output data name, and the value is a list indicating the dimensions of the corresponding data. + For example, "action" refers to an output shape of [14], indicating 14-dimensional actions. + Importantly, `output_shapes` doesn't include batch dimension or temporal dimension. + input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), + and the value specifies the normalization mode to apply. The two available modes are "mean_std" + which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a + [-1, 1] range. Note that here this defaults to None meaning inputs are not normalized. This is to + match the original implementation. + output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the + original scale. Note that this is also used for normalizing the training targets. NOTE: Clipping + to [-1, +1] is used during MPPI/CEM. Therefore, it is recommended that you stick with "min_max" + normalization mode here. + image_encoder_hidden_dim: Number of channels for the convolutional layers used for image encoding. + state_encoder_hidden_dim: Hidden dimension for MLP used for state vector encoding. + latent_dim: Observation's latent embedding dimension. + q_ensemble_size: Number of Q function estimators to use in an ensemble for uncertainty estimation. + mlp_dim: Hidden dimension of MLPs used for modelling the dynamics encoder, reward function, policy + (π), Q ensemble, and V. + discount: Discount factor (γ) to use for the reinforcement learning formalism. + use_mpc: Whether to use model predictive control. The alternative is to just sample the policy model + (π) for each step. + cem_iterations: Number of iterations for the MPPI/CEM loop in MPC. + max_std: Maximum standard deviation for actions sampled from the gaussian PDF in CEM. + min_std: Minimum standard deviation for noise applied to actions sampled from the policy model (π). + Doubles up as the minimum standard deviation for actions sampled from the gaussian PDF in CEM. + n_gaussian_samples: Number of samples to draw from the gaussian distribution every CEM iteration. Must + be non-zero. + n_pi_samples: Number of samples to draw from the policy / world model rollout every CEM iteration. Can + be zero. + uncertainty_regularizer_coeff: Coefficient for the uncertainty regularization used when estimating + trajectory values (this is the λ coefficient in eqn 4 of FOWM). + n_elites: The number of elite samples to use for updating the gaussian parameters every CEM iteration. + elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the + elites, when updating the gaussian parameters for CEM. + gaussian_mean_momentum: Momentum (α) used for EMA updates of the mean parameter μ of the gaussian + parameters optimized in CEM. Updates are calculated as μ⁻ ← αμ⁻ + (1-α)μ. + max_random_shift_ratio: Maximum random shift (as a proportion of the image size) to apply to the + image(s) (in units of pixels) for training-time augmentation. If set to 0, no such augmentation + is applied. Note that the input images are assumed to be square for this augmentation. + reward_coeff: Loss weighting coefficient for the reward regression loss. + expectile_weight: Weighting (τ) used in expectile regression for the state value function (V). + v_pred < v_target is weighted by τ and v_pred >= v_target is weighted by (1-τ). τ is expected to + be in [0, 1]. Setting τ closer to 1 results in a more "optimistic" V. This is sensible to do + because v_target is obtained by evaluating the learned state-action value functions (Q) with + in-sample actions that may not be always optimal. + value_coeff: Loss weighting coefficient for both the state-action value (Q) TD loss, and the state + value (V) expectile regression loss. + consistency_coeff: Loss weighting coefficient for the consistency loss. + advantage_scaling: A factor by which the advantages are scaled prior to exponentiation for advantage + weighted regression of the policy (π) estimator parameters. Note that the exponentiated advantages + are clamped at 100.0. + pi_coeff: Loss weighting coefficient for the action regression loss. + temporal_decay_coeff: Exponential decay coefficient for decaying the loss coefficient for future time- + steps. Hint: each loss computation involves `horizon` steps worth of actions starting from the + current time step. + target_model_momentum: Momentum (α) used for EMA updates of the target models. Updates are calculated + as ϕ ← αϕ + (1-α)θ where ϕ are the parameters of the target model and θ are the parameters of the + model being trained. + """ + + # Input / output structure. + n_obs_steps: int = 1 + n_action_repeats: int = 2 + horizon: int = 5 + n_action_steps: int = 1 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.IDENTITY, + "STATE": NormalizationMode.IDENTITY, + "ENV": NormalizationMode.IDENTITY, + "ACTION": NormalizationMode.MIN_MAX, + } + ) + + # Architecture / modeling. + # Neural networks. + image_encoder_hidden_dim: int = 32 + state_encoder_hidden_dim: int = 256 + latent_dim: int = 50 + q_ensemble_size: int = 5 + mlp_dim: int = 512 + # Reinforcement learning. + discount: float = 0.9 + + # Inference. + use_mpc: bool = True + cem_iterations: int = 6 + max_std: float = 2.0 + min_std: float = 0.05 + n_gaussian_samples: int = 512 + n_pi_samples: int = 51 + uncertainty_regularizer_coeff: float = 1.0 + n_elites: int = 50 + elite_weighting_temperature: float = 0.5 + gaussian_mean_momentum: float = 0.1 + + # Training and loss computation. + max_random_shift_ratio: float = 0.0476 + # Loss coefficients. + reward_coeff: float = 0.5 + expectile_weight: float = 0.9 + value_coeff: float = 0.1 + consistency_coeff: float = 20.0 + advantage_scaling: float = 3.0 + pi_coeff: float = 0.5 + temporal_decay_coeff: float = 0.5 + # Target model. + target_model_momentum: float = 0.995 + + # Training presets + optimizer_lr: float = 3e-4 + + def __post_init__(self): + super().__post_init__() + + """Input validation (not exhaustive).""" + if self.n_gaussian_samples <= 0: + raise ValueError( + f"The number of gaussian samples for CEM should be non-zero. Got `{self.n_gaussian_samples=}`" + ) + if self.normalization_mapping["ACTION"] is not NormalizationMode.MIN_MAX: + raise ValueError( + "TD-MPC assumes the action space dimensions to all be in [-1, 1]. Therefore it is strongly " + f"advised that you stick with the default. See {self.__class__.__name__} docstring for more " + "information." + ) + if self.n_obs_steps != 1: + raise ValueError( + f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`" + ) + if self.n_action_steps > 1: + if self.n_action_repeats != 1: + raise ValueError( + "If `n_action_steps > 1`, `n_action_repeats` must be left to its default value of 1." + ) + if not self.use_mpc: + raise ValueError("If `n_action_steps > 1`, `use_mpc` must be set to `True`.") + if self.n_action_steps > self.horizon: + raise ValueError("`n_action_steps` must be less than or equal to `horizon`.") + + def get_optimizer_preset(self) -> AdamConfig: + return AdamConfig(lr=self.optimizer_lr) + + def get_scheduler_preset(self) -> None: + return None + + def validate_features(self) -> None: + # There should only be one image key. + if len(self.image_features) > 1: + raise ValueError( + f"{self.__class__.__name__} handles at most one image for now. Got image keys {self.image_features}." + ) + + if len(self.image_features) > 0: + image_ft = next(iter(self.image_features.values())) + if image_ft.shape[-2] != image_ft.shape[-1]: + # TODO(alexander-soare): This limitation is solely because of code in the random shift + # augmentation. It should be able to be removed. + raise ValueError(f"Only square images are handled now. Got image shape {image_ft.shape}.") + + @property + def observation_delta_indices(self) -> list: + return list(range(self.horizon + 1)) + + @property + def action_delta_indices(self) -> list: + return list(range(self.horizon)) + + @property + def reward_delta_indices(self) -> None: + return list(range(self.horizon)) diff --git a/lerobot/common/policies/tdmpc/modeling_tdmpc.py b/lerobot/common/policies/tdmpc/modeling_tdmpc.py new file mode 100644 index 0000000000000000000000000000000000000000..e85dcfd81360b5ba4c9edce3d9a705da0a7fdebb --- /dev/null +++ b/lerobot/common/policies/tdmpc/modeling_tdmpc.py @@ -0,0 +1,828 @@ +#!/usr/bin/env python + +# Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su, +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Implementation of Finetuning Offline World Models in the Real World. + +The comments in this code may sometimes refer to these references: + TD-MPC paper: Temporal Difference Learning for Model Predictive Control (https://huggingface.co/papers/2203.04955) + FOWM paper: Finetuning Offline World Models in the Real World (https://huggingface.co/papers/2310.16029) +""" + +# ruff: noqa: N806 + +from collections import deque +from copy import deepcopy +from functools import partial +from typing import Callable + +import einops +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F # noqa: N812 +from torch import Tensor + +from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig +from lerobot.common.policies.utils import get_device_from_parameters, get_output_shape, populate_queues + + +class TDMPCPolicy(PreTrainedPolicy): + """Implementation of TD-MPC learning + inference. + + Please note several warnings for this policy. + - Evaluation of pretrained weights created with the original FOWM code + (https://github.com/fyhMer/fowm) works as expected. To be precise: we trained and evaluated a + model with the FOWM code for the xarm_lift_medium_replay dataset. We ported the weights across + to LeRobot, and were able to evaluate with the same success metric. BUT, we had to use inter- + process communication to use the xarm environment from FOWM. This is because our xarm + environment uses newer dependencies and does not match the environment in FOWM. See + https://github.com/huggingface/lerobot/pull/103 for implementation details. + - We have NOT checked that training on LeRobot reproduces the results from FOWM. + - Nevertheless, we have verified that we can train TD-MPC for PushT. See + `lerobot/configs/policy/tdmpc_pusht_keypoints.yaml`. + - Our current xarm datasets were generated using the environment from FOWM. Therefore they do not + match our xarm environment. + """ + + config_class = TDMPCConfig + name = "tdmpc" + + def __init__(self, config: TDMPCConfig, dataset_stats: dict[str, dict[str, Tensor]] | None = None): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + super().__init__(config) + config.validate_features() + self.config = config + + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + self.model = TDMPCTOLD(config) + self.model_target = deepcopy(self.model) + for param in self.model_target.parameters(): + param.requires_grad = False + + self.reset() + + def get_optim_params(self) -> dict: + return self.parameters() + + def reset(self): + """ + Clear observation and action queues. Clear previous means for warm starting of MPPI/CEM. Should be + called on `env.reset()` + """ + self._queues = { + "observation.state": deque(maxlen=1), + "action": deque(maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)), + } + if self.config.image_features: + self._queues["observation.image"] = deque(maxlen=1) + if self.config.env_state_feature: + self._queues["observation.environment_state"] = deque(maxlen=1) + # Previous mean obtained from the cross-entropy method (CEM) used during MPC. It is used to warm start + # CEM for the next step. + self._prev_mean: torch.Tensor | None = None + + @torch.no_grad() + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations.""" + batch = self.normalize_inputs(batch) + if self.config.image_features: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.image"] = batch[next(iter(self.config.image_features))] + + self._queues = populate_queues(self._queues, batch) + + # When the action queue is depleted, populate it again by querying the policy. + if len(self._queues["action"]) == 0: + batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch if key in self._queues} + + # Remove the time dimensions as it is not handled yet. + for key in batch: + assert batch[key].shape[1] == 1 + batch[key] = batch[key][:, 0] + + # NOTE: Order of observations matters here. + encode_keys = [] + if self.config.image_features: + encode_keys.append("observation.image") + if self.config.env_state_feature: + encode_keys.append("observation.environment_state") + encode_keys.append("observation.state") + z = self.model.encode({k: batch[k] for k in encode_keys}) + if self.config.use_mpc: # noqa: SIM108 + actions = self.plan(z) # (horizon, batch, action_dim) + else: + # Plan with the policy (π) alone. This always returns one action so unsqueeze to get a + # sequence dimension like in the MPC branch. + actions = self.model.pi(z).unsqueeze(0) + + actions = torch.clamp(actions, -1, +1) + + actions = self.unnormalize_outputs({"action": actions})["action"] + + if self.config.n_action_repeats > 1: + for _ in range(self.config.n_action_repeats): + self._queues["action"].append(actions[0]) + else: + # Action queue is (n_action_steps, batch_size, action_dim), so we transpose the action. + self._queues["action"].extend(actions[: self.config.n_action_steps]) + + action = self._queues["action"].popleft() + return action + + @torch.no_grad() + def plan(self, z: Tensor) -> Tensor: + """Plan sequence of actions using TD-MPC inference. + + Args: + z: (batch, latent_dim,) tensor for the initial state. + Returns: + (horizon, batch, action_dim,) tensor for the planned trajectory of actions. + """ + device = get_device_from_parameters(self) + + batch_size = z.shape[0] + + # Sample Nπ trajectories from the policy. + pi_actions = torch.empty( + self.config.horizon, + self.config.n_pi_samples, + batch_size, + self.config.action_feature.shape[0], + device=device, + ) + if self.config.n_pi_samples > 0: + _z = einops.repeat(z, "b d -> n b d", n=self.config.n_pi_samples) + for t in range(self.config.horizon): + # Note: Adding a small amount of noise here doesn't hurt during inference and may even be + # helpful for CEM. + pi_actions[t] = self.model.pi(_z, self.config.min_std) + _z = self.model.latent_dynamics(_z, pi_actions[t]) + + # In the CEM loop we will need this for a call to estimate_value with the gaussian sampled + # trajectories. + z = einops.repeat(z, "b d -> n b d", n=self.config.n_gaussian_samples + self.config.n_pi_samples) + + # Model Predictive Path Integral (MPPI) with the cross-entropy method (CEM) as the optimization + # algorithm. + # The initial mean and standard deviation for the cross-entropy method (CEM). + mean = torch.zeros( + self.config.horizon, batch_size, self.config.action_feature.shape[0], device=device + ) + # Maybe warm start CEM with the mean from the previous step. + if self._prev_mean is not None: + mean[:-1] = self._prev_mean[1:] + std = self.config.max_std * torch.ones_like(mean) + + for _ in range(self.config.cem_iterations): + # Randomly sample action trajectories for the gaussian distribution. + std_normal_noise = torch.randn( + self.config.horizon, + self.config.n_gaussian_samples, + batch_size, + self.config.action_feature.shape[0], + device=std.device, + ) + gaussian_actions = torch.clamp(mean.unsqueeze(1) + std.unsqueeze(1) * std_normal_noise, -1, 1) + + # Compute elite actions. + actions = torch.cat([gaussian_actions, pi_actions], dim=1) + value = self.estimate_value(z, actions).nan_to_num_(0) + elite_idxs = torch.topk(value, self.config.n_elites, dim=0).indices # (n_elites, batch) + elite_value = value.take_along_dim(elite_idxs, dim=0) # (n_elites, batch) + # (horizon, n_elites, batch, action_dim) + elite_actions = actions.take_along_dim(einops.rearrange(elite_idxs, "n b -> 1 n b 1"), dim=1) + + # Update gaussian PDF parameters to be the (weighted) mean and standard deviation of the elites. + max_value = elite_value.max(0, keepdim=True)[0] # (1, batch) + # The weighting is a softmax over trajectory values. Note that this is not the same as the usage + # of Ω in eqn 4 of the TD-MPC paper. Instead it is the normalized version of it: s = Ω/ΣΩ. This + # makes the equations: μ = Σ(s⋅Γ), σ = Σ(s⋅(Γ-μ)²). + score = torch.exp(self.config.elite_weighting_temperature * (elite_value - max_value)) + score /= score.sum(axis=0, keepdim=True) + # (horizon, batch, action_dim) + _mean = torch.sum(einops.rearrange(score, "n b -> n b 1") * elite_actions, dim=1) + _std = torch.sqrt( + torch.sum( + einops.rearrange(score, "n b -> n b 1") + * (elite_actions - einops.rearrange(_mean, "h b d -> h 1 b d")) ** 2, + dim=1, + ) + ) + # Update mean with an exponential moving average, and std with a direct replacement. + mean = ( + self.config.gaussian_mean_momentum * mean + (1 - self.config.gaussian_mean_momentum) * _mean + ) + std = _std.clamp_(self.config.min_std, self.config.max_std) + + # Keep track of the mean for warm-starting subsequent steps. + self._prev_mean = mean + + # Randomly select one of the elite actions from the last iteration of MPPI/CEM using the softmax + # scores from the last iteration. + actions = elite_actions[:, torch.multinomial(score.T, 1).squeeze(), torch.arange(batch_size)] + + return actions + + @torch.no_grad() + def estimate_value(self, z: Tensor, actions: Tensor): + """Estimates the value of a trajectory as per eqn 4 of the FOWM paper. + + Args: + z: (batch, latent_dim) tensor of initial latent states. + actions: (horizon, batch, action_dim) tensor of action trajectories. + Returns: + (batch,) tensor of values. + """ + # Initialize return and running discount factor. + G, running_discount = 0, 1 + # Iterate over the actions in the trajectory to simulate the trajectory using the latent dynamics + # model. Keep track of return. + for t in range(actions.shape[0]): + # We will compute the reward in a moment. First compute the uncertainty regularizer from eqn 4 + # of the FOWM paper. + if self.config.uncertainty_regularizer_coeff > 0: + regularization = -( + self.config.uncertainty_regularizer_coeff * self.model.Qs(z, actions[t]).std(0) + ) + else: + regularization = 0 + # Estimate the next state (latent) and reward. + z, reward = self.model.latent_dynamics_and_reward(z, actions[t]) + # Update the return and running discount. + G += running_discount * (reward + regularization) + running_discount *= self.config.discount + # Add the estimated value of the final state (using the minimum for a conservative estimate). + # Do so by predicting the next action, then taking a minimum over the ensemble of state-action value + # estimators. + # Note: This small amount of added noise seems to help a bit at inference time as observed by success + # metrics over 50 episodes of xarm_lift_medium_replay. + next_action = self.model.pi(z, self.config.min_std) # (batch, action_dim) + terminal_values = self.model.Qs(z, next_action) # (ensemble, batch) + # Randomly choose 2 of the Qs for terminal value estimation (as in App C. of the FOWM paper). + if self.config.q_ensemble_size > 2: + G += ( + running_discount + * torch.min(terminal_values[torch.randint(0, self.config.q_ensemble_size, size=(2,))], dim=0)[ + 0 + ] + ) + else: + G += running_discount * torch.min(terminal_values, dim=0)[0] + # Finally, also regularize the terminal value. + if self.config.uncertainty_regularizer_coeff > 0: + G -= running_discount * self.config.uncertainty_regularizer_coeff * terminal_values.std(0) + return G + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: + """Run the batch through the model and compute the loss. + + Returns a dictionary with loss as a tensor, and other information as native floats. + """ + device = get_device_from_parameters(self) + + batch = self.normalize_inputs(batch) + if self.config.image_features: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.image"] = batch[next(iter(self.config.image_features))] + batch = self.normalize_targets(batch) + + info = {} + + # (b, t) -> (t, b) + for key in batch: + if isinstance(batch[key], torch.Tensor) and batch[key].ndim > 1: + batch[key] = batch[key].transpose(1, 0) + + action = batch["action"] # (t, b, action_dim) + reward = batch["next.reward"] # (t, b) + observations = {k: v for k, v in batch.items() if k.startswith("observation.")} + + # Apply random image augmentations. + if self.config.image_features and self.config.max_random_shift_ratio > 0: + observations["observation.image"] = flatten_forward_unflatten( + partial(random_shifts_aug, max_random_shift_ratio=self.config.max_random_shift_ratio), + observations["observation.image"], + ) + + # Get the current observation for predicting trajectories, and all future observations for use in + # the latent consistency loss and TD loss. + current_observation, next_observations = {}, {} + for k in observations: + current_observation[k] = observations[k][0] + next_observations[k] = observations[k][1:] + horizon, batch_size = next_observations[ + "observation.image" if self.config.image_features else "observation.environment_state" + ].shape[:2] + + # Run latent rollout using the latent dynamics model and policy model. + # Note this has shape `horizon+1` because there are `horizon` actions and a current `z`. Each action + # gives us a next `z`. + batch_size = batch["index"].shape[0] + z_preds = torch.empty(horizon + 1, batch_size, self.config.latent_dim, device=device) + z_preds[0] = self.model.encode(current_observation) + reward_preds = torch.empty_like(reward, device=device) + for t in range(horizon): + z_preds[t + 1], reward_preds[t] = self.model.latent_dynamics_and_reward(z_preds[t], action[t]) + + # Compute Q and V value predictions based on the latent rollout. + q_preds_ensemble = self.model.Qs(z_preds[:-1], action) # (ensemble, horizon, batch) + v_preds = self.model.V(z_preds[:-1]) + info.update({"Q": q_preds_ensemble.mean().item(), "V": v_preds.mean().item()}) + + # Compute various targets with stopgrad. + with torch.no_grad(): + # Latent state consistency targets. + z_targets = self.model_target.encode(next_observations) + # State-action value targets (or TD targets) as in eqn 3 of the FOWM. Unlike TD-MPC which uses the + # learned state-action value function in conjunction with the learned policy: Q(z, π(z)), FOWM + # uses a learned state value function: V(z). This means the TD targets only depend on in-sample + # actions (not actions estimated by π). + # Note: Here we do not use self.model_target, but self.model. This is to follow the original code + # and the FOWM paper. + q_targets = reward + self.config.discount * self.model.V(self.model.encode(next_observations)) + # From eqn 3 of FOWM. These appear as Q(z, a). Here we call them v_targets to emphasize that we + # are using them to compute loss for V. + v_targets = self.model_target.Qs(z_preds[:-1].detach(), action, return_min=True) + + # Compute losses. + # Exponentially decay the loss weight with respect to the timestep. Steps that are more distant in the + # future have less impact on the loss. Note: unsqueeze will let us broadcast to (seq, batch). + temporal_loss_coeffs = torch.pow( + self.config.temporal_decay_coeff, torch.arange(horizon, device=device) + ).unsqueeze(-1) + # Compute consistency loss as MSE loss between latents predicted from the rollout and latents + # predicted from the (target model's) observation encoder. + consistency_loss = ( + ( + temporal_loss_coeffs + * F.mse_loss(z_preds[1:], z_targets, reduction="none").mean(dim=-1) + # `z_preds` depends on the current observation and the actions. + * ~batch["observation.state_is_pad"][0] + * ~batch["action_is_pad"] + # `z_targets` depends on the next observation. + * ~batch["observation.state_is_pad"][1:] + ) + .sum(0) + .mean() + ) + # Compute the reward loss as MSE loss between rewards predicted from the rollout and the dataset + # rewards. + reward_loss = ( + ( + temporal_loss_coeffs + * F.mse_loss(reward_preds, reward, reduction="none") + * ~batch["next.reward_is_pad"] + # `reward_preds` depends on the current observation and the actions. + * ~batch["observation.state_is_pad"][0] + * ~batch["action_is_pad"] + ) + .sum(0) + .mean() + ) + # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble. + q_value_loss = ( + ( + temporal_loss_coeffs + * F.mse_loss( + q_preds_ensemble, + einops.repeat(q_targets, "t b -> e t b", e=q_preds_ensemble.shape[0]), + reduction="none", + ).sum(0) # sum over ensemble + # `q_preds_ensemble` depends on the first observation and the actions. + * ~batch["observation.state_is_pad"][0] + * ~batch["action_is_pad"] + # q_targets depends on the reward and the next observations. + * ~batch["next.reward_is_pad"] + * ~batch["observation.state_is_pad"][1:] + ) + .sum(0) + .mean() + ) + # Compute state value loss as in eqn 3 of FOWM. + diff = v_targets - v_preds + # Expectile loss penalizes: + # - `v_preds < v_targets` with weighting `expectile_weight` + # - `v_preds >= v_targets` with weighting `1 - expectile_weight` + raw_v_value_loss = torch.where( + diff > 0, self.config.expectile_weight, (1 - self.config.expectile_weight) + ) * (diff**2) + v_value_loss = ( + ( + temporal_loss_coeffs + * raw_v_value_loss + # `v_targets` depends on the first observation and the actions, as does `v_preds`. + * ~batch["observation.state_is_pad"][0] + * ~batch["action_is_pad"] + ) + .sum(0) + .mean() + ) + + # Calculate the advantage weighted regression loss for π as detailed in FOWM 3.1. + # We won't need these gradients again so detach. + z_preds = z_preds.detach() + # Use stopgrad for the advantage calculation. + with torch.no_grad(): + advantage = self.model_target.Qs(z_preds[:-1], action, return_min=True) - self.model.V( + z_preds[:-1] + ) + info["advantage"] = advantage[0] + # (t, b) + exp_advantage = torch.clamp(torch.exp(advantage * self.config.advantage_scaling), max=100.0) + action_preds = self.model.pi(z_preds[:-1]) # (t, b, a) + # Calculate the MSE between the actions and the action predictions. + # Note: FOWM's original code calculates the log probability (wrt to a unit standard deviation + # gaussian) and sums over the action dimension. Computing the (negative) log probability amounts to + # multiplying the MSE by 0.5 and adding a constant offset (the log(2*pi)/2 term, times the action + # dimension). Here we drop the constant offset as it doesn't change the optimization step, and we drop + # the 0.5 as we instead make a configuration parameter for it (see below where we compute the total + # loss). + mse = F.mse_loss(action_preds, action, reduction="none").sum(-1) # (t, b) + # NOTE: The original implementation does not take the sum over the temporal dimension like with the + # other losses. + # TODO(alexander-soare): Take the sum over the temporal dimension and check that training still works + # as well as expected. + pi_loss = ( + exp_advantage + * mse + * temporal_loss_coeffs + # `action_preds` depends on the first observation and the actions. + * ~batch["observation.state_is_pad"][0] + * ~batch["action_is_pad"] + ).mean() + + loss = ( + self.config.consistency_coeff * consistency_loss + + self.config.reward_coeff * reward_loss + + self.config.value_coeff * q_value_loss + + self.config.value_coeff * v_value_loss + + self.config.pi_coeff * pi_loss + ) + + info.update( + { + "consistency_loss": consistency_loss.item(), + "reward_loss": reward_loss.item(), + "Q_value_loss": q_value_loss.item(), + "V_value_loss": v_value_loss.item(), + "pi_loss": pi_loss.item(), + "sum_loss": loss.item() * self.config.horizon, + } + ) + + # Undo (b, t) -> (t, b). + for key in batch: + if isinstance(batch[key], torch.Tensor) and batch[key].ndim > 1: + batch[key] = batch[key].transpose(1, 0) + + return loss, info + + def update(self): + """Update the target model's parameters with an EMA step.""" + # Note a minor variation with respect to the original FOWM code. Here they do this based on an EMA + # update frequency parameter which is set to 2 (every 2 steps an update is done). To simplify the code + # we update every step and adjust the decay parameter `alpha` accordingly (0.99 -> 0.995) + update_ema_parameters(self.model_target, self.model, self.config.target_model_momentum) + + +class TDMPCTOLD(nn.Module): + """Task-Oriented Latent Dynamics (TOLD) model used in TD-MPC.""" + + def __init__(self, config: TDMPCConfig): + super().__init__() + self.config = config + self._encoder = TDMPCObservationEncoder(config) + self._dynamics = nn.Sequential( + nn.Linear(config.latent_dim + config.action_feature.shape[0], config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Mish(), + nn.Linear(config.mlp_dim, config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Mish(), + nn.Linear(config.mlp_dim, config.latent_dim), + nn.LayerNorm(config.latent_dim), + nn.Sigmoid(), + ) + self._reward = nn.Sequential( + nn.Linear(config.latent_dim + config.action_feature.shape[0], config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Mish(), + nn.Linear(config.mlp_dim, config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Mish(), + nn.Linear(config.mlp_dim, 1), + ) + self._pi = nn.Sequential( + nn.Linear(config.latent_dim, config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Mish(), + nn.Linear(config.mlp_dim, config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Mish(), + nn.Linear(config.mlp_dim, config.action_feature.shape[0]), + ) + self._Qs = nn.ModuleList( + [ + nn.Sequential( + nn.Linear(config.latent_dim + config.action_feature.shape[0], config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Tanh(), + nn.Linear(config.mlp_dim, config.mlp_dim), + nn.ELU(), + nn.Linear(config.mlp_dim, 1), + ) + for _ in range(config.q_ensemble_size) + ] + ) + self._V = nn.Sequential( + nn.Linear(config.latent_dim, config.mlp_dim), + nn.LayerNorm(config.mlp_dim), + nn.Tanh(), + nn.Linear(config.mlp_dim, config.mlp_dim), + nn.ELU(), + nn.Linear(config.mlp_dim, 1), + ) + self._init_weights() + + def _init_weights(self): + """Initialize model weights. + + Orthogonal initialization for all linear and convolutional layers' weights (apart from final layers + of reward network and Q networks which get zero initialization). + Zero initialization for all linear and convolutional layers' biases. + """ + + def _apply_fn(m): + if isinstance(m, nn.Linear): + nn.init.orthogonal_(m.weight.data) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Conv2d): + gain = nn.init.calculate_gain("relu") + nn.init.orthogonal_(m.weight.data, gain) + if m.bias is not None: + nn.init.zeros_(m.bias) + + self.apply(_apply_fn) + for m in [self._reward, *self._Qs]: + assert isinstance(m[-1], nn.Linear), ( + "Sanity check. The last linear layer needs 0 initialization on weights." + ) + nn.init.zeros_(m[-1].weight) + nn.init.zeros_(m[-1].bias) # this has already been done, but keep this line here for good measure + + def encode(self, obs: dict[str, Tensor]) -> Tensor: + """Encodes an observation into its latent representation.""" + return self._encoder(obs) + + def latent_dynamics_and_reward(self, z: Tensor, a: Tensor) -> tuple[Tensor, Tensor]: + """Predict the next state's latent representation and the reward given a current latent and action. + + Args: + z: (*, latent_dim) tensor for the current state's latent representation. + a: (*, action_dim) tensor for the action to be applied. + Returns: + A tuple containing: + - (*, latent_dim) tensor for the next state's latent representation. + - (*,) tensor for the estimated reward. + """ + x = torch.cat([z, a], dim=-1) + return self._dynamics(x), self._reward(x).squeeze(-1) + + def latent_dynamics(self, z: Tensor, a: Tensor) -> Tensor: + """Predict the next state's latent representation given a current latent and action. + + Args: + z: (*, latent_dim) tensor for the current state's latent representation. + a: (*, action_dim) tensor for the action to be applied. + Returns: + (*, latent_dim) tensor for the next state's latent representation. + """ + x = torch.cat([z, a], dim=-1) + return self._dynamics(x) + + def pi(self, z: Tensor, std: float = 0.0) -> Tensor: + """Samples an action from the learned policy. + + The policy can also have added (truncated) Gaussian noise injected for encouraging exploration when + generating rollouts for online training. + + Args: + z: (*, latent_dim) tensor for the current state's latent representation. + std: The standard deviation of the injected noise. + Returns: + (*, action_dim) tensor for the sampled action. + """ + action = torch.tanh(self._pi(z)) + if std > 0: + std = torch.ones_like(action) * std + action += torch.randn_like(action) * std + return action + + def V(self, z: Tensor) -> Tensor: # noqa: N802 + """Predict state value (V). + + Args: + z: (*, latent_dim) tensor for the current state's latent representation. + Returns: + (*,) tensor of estimated state values. + """ + return self._V(z).squeeze(-1) + + def Qs(self, z: Tensor, a: Tensor, return_min: bool = False) -> Tensor: # noqa: N802 + """Predict state-action value for all of the learned Q functions. + + Args: + z: (*, latent_dim) tensor for the current state's latent representation. + a: (*, action_dim) tensor for the action to be applied. + return_min: Set to true for implementing the detail in App. C of the FOWM paper: randomly select + 2 of the Qs and return the minimum + Returns: + (q_ensemble, *) tensor for the value predictions of each learned Q function in the ensemble OR + (*,) tensor if return_min=True. + """ + x = torch.cat([z, a], dim=-1) + if not return_min: + return torch.stack([q(x).squeeze(-1) for q in self._Qs], dim=0) + else: + if len(self._Qs) > 2: # noqa: SIM108 + Qs = [self._Qs[i] for i in np.random.choice(len(self._Qs), size=2)] + else: + Qs = self._Qs + return torch.stack([q(x).squeeze(-1) for q in Qs], dim=0).min(dim=0)[0] + + +class TDMPCObservationEncoder(nn.Module): + """Encode image and/or state vector observations.""" + + def __init__(self, config: TDMPCConfig): + """ + Creates encoders for pixel and/or state modalities. + TODO(alexander-soare): The original work allows for multiple images by concatenating them along the + channel dimension. Re-implement this capability. + """ + super().__init__() + self.config = config + + if config.image_features: + self.image_enc_layers = nn.Sequential( + nn.Conv2d( + next(iter(config.image_features.values())).shape[0], + config.image_encoder_hidden_dim, + 7, + stride=2, + ), + nn.ReLU(), + nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 5, stride=2), + nn.ReLU(), + nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2), + nn.ReLU(), + nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2), + nn.ReLU(), + ) + dummy_shape = (1, *next(iter(config.image_features.values())).shape) + out_shape = get_output_shape(self.image_enc_layers, dummy_shape)[1:] + self.image_enc_layers.extend( + nn.Sequential( + nn.Flatten(), + nn.Linear(np.prod(out_shape), config.latent_dim), + nn.LayerNorm(config.latent_dim), + nn.Sigmoid(), + ) + ) + + if config.robot_state_feature: + self.state_enc_layers = nn.Sequential( + nn.Linear(config.robot_state_feature.shape[0], config.state_encoder_hidden_dim), + nn.ELU(), + nn.Linear(config.state_encoder_hidden_dim, config.latent_dim), + nn.LayerNorm(config.latent_dim), + nn.Sigmoid(), + ) + + if config.env_state_feature: + self.env_state_enc_layers = nn.Sequential( + nn.Linear(config.env_state_feature.shape[0], config.state_encoder_hidden_dim), + nn.ELU(), + nn.Linear(config.state_encoder_hidden_dim, config.latent_dim), + nn.LayerNorm(config.latent_dim), + nn.Sigmoid(), + ) + + def forward(self, obs_dict: dict[str, Tensor]) -> Tensor: + """Encode the image and/or state vector. + + Each modality is encoded into a feature vector of size (latent_dim,) and then a uniform mean is taken + over all features. + """ + feat = [] + # NOTE: Order of observations matters here. + if self.config.image_features: + feat.append( + flatten_forward_unflatten( + self.image_enc_layers, obs_dict[next(iter(self.config.image_features))] + ) + ) + if self.config.env_state_feature: + feat.append(self.env_state_enc_layers(obs_dict[OBS_ENV_STATE])) + if self.config.robot_state_feature: + feat.append(self.state_enc_layers(obs_dict[OBS_STATE])) + return torch.stack(feat, dim=0).mean(0) + + +def random_shifts_aug(x: Tensor, max_random_shift_ratio: float) -> Tensor: + """Randomly shifts images horizontally and vertically. + + Adapted from https://github.com/facebookresearch/drqv2 + """ + b, _, h, w = x.size() + assert h == w, "non-square images not handled yet" + pad = int(round(max_random_shift_ratio * h)) + x = F.pad(x, tuple([pad] * 4), "replicate") + eps = 1.0 / (h + 2 * pad) + arange = torch.linspace( + -1.0 + eps, + 1.0 - eps, + h + 2 * pad, + device=x.device, + dtype=torch.float32, + )[:h] + arange = einops.repeat(arange, "w -> h w 1", h=h) + base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2) + base_grid = einops.repeat(base_grid, "h w c -> b h w c", b=b) + # A random shift in units of pixels and within the boundaries of the padding. + shift = torch.randint( + 0, + 2 * pad + 1, + size=(b, 1, 1, 2), + device=x.device, + dtype=torch.float32, + ) + shift *= 2.0 / (h + 2 * pad) + grid = base_grid + shift + return F.grid_sample(x, grid, padding_mode="zeros", align_corners=False) + + +def update_ema_parameters(ema_net: nn.Module, net: nn.Module, alpha: float): + """Update EMA parameters in place with ema_param <- alpha * ema_param + (1 - alpha) * param.""" + for ema_module, module in zip(ema_net.modules(), net.modules(), strict=True): + for (n_p_ema, p_ema), (n_p, p) in zip( + ema_module.named_parameters(recurse=False), module.named_parameters(recurse=False), strict=True + ): + assert n_p_ema == n_p, "Parameter names don't match for EMA model update" + if isinstance(p, dict): + raise RuntimeError("Dict parameter not supported") + if isinstance(module, nn.modules.batchnorm._BatchNorm) or not p.requires_grad: + # Copy BatchNorm parameters, and non-trainable parameters directly. + p_ema.copy_(p.to(dtype=p_ema.dtype).data) + with torch.no_grad(): + p_ema.mul_(alpha) + p_ema.add_(p.to(dtype=p_ema.dtype).data, alpha=1 - alpha) + + +def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tensor) -> Tensor: + """Helper to temporarily flatten extra dims at the start of the image tensor. + + Args: + fn: Callable that the image tensor will be passed to. It should accept (B, C, H, W) and return + (B, *), where * is any number of dimensions. + image_tensor: An image tensor of shape (**, C, H, W), where ** is any number of dimensions, generally + different from *. + Returns: + A return value from the callable reshaped to (**, *). + """ + if image_tensor.ndim == 4: + return fn(image_tensor) + start_dims = image_tensor.shape[:-3] + inp = torch.flatten(image_tensor, end_dim=-4) + flat_out = fn(inp) + return torch.reshape(flat_out, (*start_dims, *flat_out.shape[1:])) diff --git a/lerobot/common/policies/utils.py b/lerobot/common/policies/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..12b2af3642cd0017e185989ba3ed3a73adb44222 --- /dev/null +++ b/lerobot/common/policies/utils.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections import deque + +import torch +from torch import nn + + +def populate_queues( + queues: dict[str, deque], batch: dict[str, torch.Tensor], exclude_keys: list[str] | None = None +): + if exclude_keys is None: + exclude_keys = [] + for key in batch: + # Ignore keys not in the queues already (leaving the responsibility to the caller to make sure the + # queues have the keys they want). + if key not in queues or key in exclude_keys: + continue + if len(queues[key]) != queues[key].maxlen: + # initialize by copying the first observation several times until the queue is full + while len(queues[key]) != queues[key].maxlen: + queues[key].append(batch[key]) + else: + # add latest observation to the queue + queues[key].append(batch[key]) + return queues + + +def get_device_from_parameters(module: nn.Module) -> torch.device: + """Get a module's device by checking one of its parameters. + + Note: assumes that all parameters have the same device + """ + return next(iter(module.parameters())).device + + +def get_dtype_from_parameters(module: nn.Module) -> torch.dtype: + """Get a module's parameter dtype by checking one of its parameters. + + Note: assumes that all parameters have the same dtype. + """ + return next(iter(module.parameters())).dtype + + +def get_output_shape(module: nn.Module, input_shape: tuple) -> tuple: + """ + Calculates the output shape of a PyTorch module given an input shape. + + Args: + module (nn.Module): a PyTorch module + input_shape (tuple): A tuple representing the input shape, e.g., (batch_size, channels, height, width) + + Returns: + tuple: The output shape of the module. + """ + dummy_input = torch.zeros(size=input_shape) + with torch.inference_mode(): + output = module(dummy_input) + return tuple(output.shape) diff --git a/lerobot/common/policies/vqbet/configuration_vqbet.py b/lerobot/common/policies/vqbet/configuration_vqbet.py new file mode 100644 index 0000000000000000000000000000000000000000..7cea825c2a8cdc3962472959f3c6a9215b37ccef --- /dev/null +++ b/lerobot/common/policies/vqbet/configuration_vqbet.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python + +# Copyright 2024 Seungjae Lee and Yibin Wang and Haritheja Etukuru +# and H. Jin Kim and Nur Muhammad Mahi Shafiullah and Lerrel Pinto +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.optim.optimizers import AdamConfig +from lerobot.common.optim.schedulers import VQBeTSchedulerConfig +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import NormalizationMode + + +@PreTrainedConfig.register_subclass("vqbet") +@dataclass +class VQBeTConfig(PreTrainedConfig): + """Configuration class for VQ-BeT. + + Defaults are configured for training with PushT providing proprioceptive and single camera observations. + + The parameters you will most likely need to change are the ones which depend on the environment / sensors. + Those are: `input_shapes` and `output_shapes`. + + Notes on the inputs and outputs: + - "observation.state" is required as an input key. + - At least one key starting with "observation.image is required as an input. + - If there are multiple keys beginning with "observation.image" they are treated as multiple camera + views. Right now we only support all images having the same shape. + - "action" is required as an output key. + + Args: + n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the + current step and additional steps going back). + n_action_pred_token: Total number of current token and future tokens that VQ-BeT predicts. + action_chunk_size: Action chunk size of each action prediction token. + input_shapes: A dictionary defining the shapes of the input data for the policy. + The key represents the input data name, and the value is a list indicating the dimensions + of the corresponding data. For example, "observation.image" refers to an input from + a camera with dimensions [3, 96, 96], indicating it has three color channels and 96x96 resolution. + Importantly, shapes doesnt include batch dimension or temporal dimension. + output_shapes: A dictionary defining the shapes of the output data for the policy. + The key represents the output data name, and the value is a list indicating the dimensions + of the corresponding data. For example, "action" refers to an output shape of [14], indicating + 14-dimensional actions. Importantly, shapes doesnt include batch dimension or temporal dimension. + input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), + and the value specifies the normalization mode to apply. The two available modes are "mean_std" + which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a + [-1, 1] range. + output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the + original scale. Note that this is also used for normalizing the training targets. + vision_backbone: Name of the torchvision resnet backbone to use for encoding images. + crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit + within the image size. If None, no cropping is done. + crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval + mode). + pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone. + `None` means no pretrained weights. + use_group_norm: Whether to replace batch normalization with group normalization in the backbone. + The group sizes are set to be about 16 (to be precise, feature_dim // 16). + spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax. + n_vqvae_training_steps: Number of optimization steps for training Residual VQ. + vqvae_n_embed: Number of embedding vectors in the RVQ dictionary (each layer). + vqvae_embedding_dim: Dimension of each embedding vector in the RVQ dictionary. + vqvae_enc_hidden_dim: Size of hidden dimensions of Encoder / Decoder part of Residaul VQ-VAE + gpt_block_size: Max block size of minGPT (should be larger than the number of input tokens) + gpt_input_dim: Size of output input of GPT. This is also used as the dimension of observation features. + gpt_output_dim: Size of output dimension of GPT. This is also used as a input dimension of offset / bin prediction headers. + gpt_n_layer: Number of layers of GPT + gpt_n_head: Number of headers of GPT + gpt_hidden_dim: Size of hidden dimensions of GPT + dropout: Dropout rate for GPT + mlp_hidden_dim: Size of hidden dimensions of offset header / bin prediction headers parts of VQ-BeT + offset_loss_weight: A constant that is multiplied to the offset loss + primary_code_loss_weight: A constant that is multiplied to the primary code prediction loss + secondary_code_loss_weight: A constant that is multiplied to the secondary code prediction loss + bet_softmax_temperature: Sampling temperature of code for rollout with VQ-BeT + sequentially_select: Whether select code of primary / secondary as sequentially (pick primary code, + and then select secodnary code), or at the same time. + """ + + # Inputs / output structure. + n_obs_steps: int = 5 + n_action_pred_token: int = 3 + action_chunk_size: int = 5 + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.IDENTITY, + "STATE": NormalizationMode.MIN_MAX, + "ACTION": NormalizationMode.MIN_MAX, + } + ) + + # Architecture / modeling. + # Vision backbone. + vision_backbone: str = "resnet18" + crop_shape: tuple[int, int] | None = (84, 84) + crop_is_random: bool = True + pretrained_backbone_weights: str | None = None + use_group_norm: bool = True + spatial_softmax_num_keypoints: int = 32 + # VQ-VAE + n_vqvae_training_steps: int = 20000 + vqvae_n_embed: int = 16 + vqvae_embedding_dim: int = 256 + vqvae_enc_hidden_dim: int = 128 + # VQ-BeT + gpt_block_size: int = 500 + gpt_input_dim: int = 512 + gpt_output_dim: int = 512 + gpt_n_layer: int = 8 + gpt_n_head: int = 8 + gpt_hidden_dim: int = 512 + dropout: float = 0.1 + mlp_hidden_dim: int = 1024 + offset_loss_weight: float = 10000.0 + primary_code_loss_weight: float = 5.0 + secondary_code_loss_weight: float = 0.5 + bet_softmax_temperature: float = 0.1 + sequentially_select: bool = False + + # Training presets + optimizer_lr: float = 1e-4 + optimizer_betas: tuple = (0.95, 0.999) + optimizer_eps: float = 1e-8 + optimizer_weight_decay: float = 1e-6 + optimizer_vqvae_lr: float = 1e-3 + optimizer_vqvae_weight_decay: float = 1e-4 + scheduler_warmup_steps: int = 500 + + def __post_init__(self): + super().__post_init__() + + """Input validation (not exhaustive).""" + if not self.vision_backbone.startswith("resnet"): + raise ValueError( + f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}." + ) + + def get_optimizer_preset(self) -> AdamConfig: + return AdamConfig( + lr=self.optimizer_lr, + betas=self.optimizer_betas, + eps=self.optimizer_eps, + weight_decay=self.optimizer_weight_decay, + ) + + def get_scheduler_preset(self) -> VQBeTSchedulerConfig: + return VQBeTSchedulerConfig( + num_warmup_steps=self.scheduler_warmup_steps, + num_vqvae_training_steps=self.n_vqvae_training_steps, + ) + + def validate_features(self) -> None: + # Note: this check was previously performed inside VQBeTRgbEncoder in the form of + # assert len(image_keys) == 1 + if not len(self.image_features) == 1: + raise ValueError("You must provide only one image among the inputs.") + + if self.crop_shape is not None: + for key, image_ft in self.image_features.items(): + if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]: + raise ValueError( + f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} " + f"for `crop_shape` and {image_ft.shape} for " + f"`{key}`." + ) + + # Check that all input images have the same shape. + first_image_key, first_image_ft = next(iter(self.image_features.items())) + for key, image_ft in self.image_features.items(): + if image_ft.shape != first_image_ft.shape: + raise ValueError( + f"`{key}` does not match `{first_image_key}`, but we expect all image shapes to match." + ) + + @property + def observation_delta_indices(self) -> list: + return list(range(1 - self.n_obs_steps, 1)) + + @property + def action_delta_indices(self) -> list: + return list(range(1 - self.n_obs_steps, self.n_action_pred_token + self.action_chunk_size - 1)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/lerobot/common/policies/vqbet/modeling_vqbet.py b/lerobot/common/policies/vqbet/modeling_vqbet.py new file mode 100644 index 0000000000000000000000000000000000000000..311132403cb81a3ad8114e7e3c6aee709238a271 --- /dev/null +++ b/lerobot/common/policies/vqbet/modeling_vqbet.py @@ -0,0 +1,911 @@ +#!/usr/bin/env python + +# Copyright 2024 Seungjae Lee and Yibin Wang and Haritheja Etukuru +# and H. Jin Kim and Nur Muhammad Mahi Shafiullah and Lerrel Pinto +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import warnings +from collections import deque +from typing import Callable, List + +import einops +import numpy as np +import torch +import torch.nn.functional as F # noqa: N812 +import torchvision +from torch import Tensor, nn + +from lerobot.common.policies.normalize import Normalize, Unnormalize +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.utils import get_device_from_parameters, get_output_shape, populate_queues +from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig +from lerobot.common.policies.vqbet.vqbet_utils import GPT, ResidualVQ + +# ruff: noqa: N806 + + +class VQBeTPolicy(PreTrainedPolicy): + """ + VQ-BeT Policy as per "Behavior Generation with Latent Actions" + """ + + config_class = VQBeTConfig + name = "vqbet" + + def __init__( + self, + config: VQBeTConfig | None = None, + dataset_stats: dict[str, dict[str, Tensor]] | None = None, + ): + """ + Args: + config: Policy configuration class instance or None, in which case the default instantiation of + the configuration class is used. + dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected + that they will be passed with a call to `load_state_dict` before the policy is used. + """ + super().__init__(config) + config.validate_features() + self.config = config + + self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) + self.normalize_targets = Normalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + self.unnormalize_outputs = Unnormalize( + config.output_features, config.normalization_mapping, dataset_stats + ) + + self.vqbet = VQBeTModel(config) + + self.reset() + + def get_optim_params(self) -> dict: + vqvae_params = ( + list(self.vqbet.action_head.vqvae_model.encoder.parameters()) + + list(self.vqbet.action_head.vqvae_model.decoder.parameters()) + + list(self.vqbet.action_head.vqvae_model.vq_layer.parameters()) + ) + decay_params, no_decay_params = self.vqbet.policy.configure_parameters() + decay_params = ( + decay_params + + list(self.vqbet.rgb_encoder.parameters()) + + list(self.vqbet.state_projector.parameters()) + + list(self.vqbet.rgb_feature_projector.parameters()) + + [self.vqbet.action_token] + + list(self.vqbet.action_head.map_to_cbet_preds_offset.parameters()) + ) + + if self.config.sequentially_select: + decay_params = ( + decay_params + + list(self.vqbet.action_head.map_to_cbet_preds_primary_bin.parameters()) + + list(self.vqbet.action_head.map_to_cbet_preds_secondary_bin.parameters()) + ) + else: + decay_params = decay_params + list(self.vqbet.action_head.map_to_cbet_preds_bin.parameters()) + + return [ + { + "params": decay_params, + }, + { + "params": vqvae_params, + "weight_decay": self.config.optimizer_vqvae_weight_decay, + "lr": self.config.optimizer_vqvae_lr, + }, + { + "params": no_decay_params, + "weight_decay": 0.0, + }, + ] + + def reset(self): + """ + Clear observation and action queues. Should be called on `env.reset()` + queues are populated during rollout of the policy, they contain the n latest observations and actions + """ + self._queues = { + "observation.images": deque(maxlen=self.config.n_obs_steps), + "observation.state": deque(maxlen=self.config.n_obs_steps), + "action": deque(maxlen=self.config.action_chunk_size), + } + + @torch.no_grad + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations. + + This method wraps `select_actions` in order to return one action at a time for execution in the + environment. It works by managing the actions in a queue and only calling `select_actions` when the + queue is empty. + """ + + batch = self.normalize_inputs(batch) + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.images"] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) + # Note: It's important that this happens after stacking the images into a single key. + self._queues = populate_queues(self._queues, batch) + + if not self.vqbet.action_head.vqvae_model.discretized.item(): + warnings.warn( + "To evaluate in the environment, your VQ-BeT model should contain a pretrained Residual VQ.", + stacklevel=1, + ) + + if len(self._queues["action"]) == 0: + batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues} + actions = self.vqbet(batch, rollout=True)[:, : self.config.action_chunk_size] + + # the dimension of returned action is (batch_size, action_chunk_size, action_dim) + actions = self.unnormalize_outputs({"action": actions})["action"] + # since the data in the action queue's dimension is (action_chunk_size, batch_size, action_dim), we transpose the action and fill the queue + self._queues["action"].extend(actions.transpose(0, 1)) + + action = self._queues["action"].popleft() + return action + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: + """Run the batch through the model and compute the loss for training or validation.""" + batch = self.normalize_inputs(batch) + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.images"] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) + batch = self.normalize_targets(batch) + # VQ-BeT discretizes action using VQ-VAE before training BeT (please refer to section 3.2 in the VQ-BeT paper https://huggingface.co/papers/2403.03181) + if not self.vqbet.action_head.vqvae_model.discretized.item(): + # loss: total loss of training RVQ + # n_different_codes: how many of the total possible VQ codes are being used in single batch (how many of them have at least one encoder embedding as a nearest neighbor). This can be at most `vqvae_n_embed * number of layers of RVQ (=2)`. + # n_different_combinations: how many different code combinations are being used out of all possible combinations in single batch. This can be at most `vqvae_n_embed ^ number of layers of RVQ (=2)` (hint consider the RVQ as a decision tree). + loss, n_different_codes, n_different_combinations, recon_l1_error = ( + self.vqbet.action_head.discretize(self.config.n_vqvae_training_steps, batch["action"]) + ) + return loss, { + "n_different_codes": n_different_codes, + "n_different_combinations": n_different_combinations, + "recon_l1_error": recon_l1_error, + } + # if Residual VQ is already trained, VQ-BeT trains its GPT and bin prediction head / offset prediction head parts. + _, loss_dict = self.vqbet(batch, rollout=False) + loss = loss_dict.pop("loss") + + return loss, loss_dict + + +class SpatialSoftmax(nn.Module): + """ + Spatial Soft Argmax operation described in "Deep Spatial Autoencoders for Visuomotor Learning" by Finn et al. + (https://huggingface.co/papers/1509.06113). A minimal port of the robomimic implementation. + + At a high level, this takes 2D feature maps (from a convnet/ViT) and returns the "center of mass" + of activations of each channel, i.e., keypoints in the image space for the policy to focus on. + + Example: take feature maps of size (512x10x12). We generate a grid of normalized coordinates (10x12x2): + ----------------------------------------------------- + | (-1., -1.) | (-0.82, -1.) | ... | (1., -1.) | + | (-1., -0.78) | (-0.82, -0.78) | ... | (1., -0.78) | + | ... | ... | ... | ... | + | (-1., 1.) | (-0.82, 1.) | ... | (1., 1.) | + ----------------------------------------------------- + This is achieved by applying channel-wise softmax over the activations (512x120) and computing the dot + product with the coordinates (120x2) to get expected points of maximal activation (512x2). + + The example above results in 512 keypoints (corresponding to the 512 input channels). We can optionally + provide num_kp != None to control the number of keypoints. This is achieved by a first applying a learnable + linear mapping (in_channels, H, W) -> (num_kp, H, W). + """ + + def __init__(self, input_shape, num_kp=None): + """ + Args: + input_shape (list): (C, H, W) input feature map shape. + num_kp (int): number of keypoints in output. If None, output will have the same number of channels as input. + """ + super().__init__() + + assert len(input_shape) == 3 + self._in_c, self._in_h, self._in_w = input_shape + + if num_kp is not None: + self.nets = torch.nn.Conv2d(self._in_c, num_kp, kernel_size=1) + self._out_c = num_kp + else: + self.nets = None + self._out_c = self._in_c + + # we could use torch.linspace directly but that seems to behave slightly differently than numpy + # and causes a small degradation in pc_success of pre-trained models. + pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h)) + pos_x = torch.from_numpy(pos_x.reshape(self._in_h * self._in_w, 1)).float() + pos_y = torch.from_numpy(pos_y.reshape(self._in_h * self._in_w, 1)).float() + # register as buffer so it's moved to the correct device. + self.register_buffer("pos_grid", torch.cat([pos_x, pos_y], dim=1)) + + def forward(self, features: Tensor) -> Tensor: + """ + Args: + features: (B, C, H, W) input feature maps. + Returns: + (B, K, 2) image-space coordinates of keypoints. + """ + if self.nets is not None: + features = self.nets(features) + + # [B, K, H, W] -> [B * K, H * W] where K is number of keypoints + features = features.reshape(-1, self._in_h * self._in_w) + # 2d softmax normalization + attention = F.softmax(features, dim=-1) + # [B * K, H * W] x [H * W, 2] -> [B * K, 2] for spatial coordinate mean in x and y dimensions + expected_xy = attention @ self.pos_grid + # reshape to [B, K, 2] + feature_keypoints = expected_xy.view(-1, self._out_c, 2) + + return feature_keypoints + + +class VQBeTModel(nn.Module): + """VQ-BeT: The underlying neural network for VQ-BeT + + Note: In this code we use the terms `rgb_encoder`, 'policy', `action_head`. The meanings are as follows. + - The `rgb_encoder` process rgb-style image observations to one-dimensional embedding vectors + - A `policy` is a minGPT architecture, that takes observation sequences and action query tokens to generate `features`. + - These `features` pass through the action head, which passes through the code prediction, offset prediction head, + and finally generates a prediction for the action chunks. + + -------------------------------** legend **------------------------------- + │ n = n_obs_steps, p = n_action_pred_token, c = action_chunk_size) │ + │ o_{t} : visual observation at timestep {t} │ + │ s_{t} : state observation at timestep {t} │ + │ a_{t} : action at timestep {t} │ + │ A_Q : action_query_token │ + -------------------------------------------------------------------------- + + + Training Phase 1. Discretize action using Residual VQ (for config.n_vqvae_training_steps steps) + + + ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ + │ │ │ │ │ │ + │ RVQ encoder │ ─► │ Residual │ ─► │ RVQ Decoder │ + │ (a_{t}~a_{t+p}) │ │ Code Quantizer │ │ │ + │ │ │ │ │ │ + └─────────────────┘ └─────────────────┘ └─────────────────┘ + + Training Phase 2. + + timestep {t-n+1} timestep {t-n+2} timestep {t} + ┌─────┴─────┐ ┌─────┴─────┐ ┌─────┴─────┐ + + o_{t-n+1} o_{t-n+2} ... o_{t} + │ │ │ + │ s_{t-n+1} │ s_{t-n+2} ... │ s_{t} p + │ │ │ │ │ │ ┌───────┴───────┐ + │ │ A_Q │ │ A_Q ... │ │ A_Q ... A_Q + │ │ │ │ │ │ │ │ │ │ + ┌───▼─────▼─────▼─────▼─────▼─────▼─────────────────▼─────▼─────▼───────────────▼───┐ + │ │ + │ GPT │ => policy + │ │ + └───────────────▼─────────────────▼─────────────────────────────▼───────────────▼───┘ + │ │ │ │ + ┌───┴───┐ ┌───┴───┐ ┌───┴───┐ ┌───┴───┐ + code offset code offset code offset code offset + ▼ │ ▼ │ ▼ │ ▼ │ => action_head + RVQ Decoder │ RVQ Decoder │ RVQ Decoder │ RVQ Decoder │ + └── + ──┘ └── + ──┘ └── + ──┘ └── + ──┘ + ▼ ▼ ▼ ▼ + action chunk action chunk action chunk action chunk + a_{t-n+1} ~ a_{t-n+2} ~ a_{t} ~ ... a_{t+p-1} ~ + a_{t-n+c} a_{t-n+c+1} a_{t+c-1} a_{t+p+c-1} + + ▼ + ONLY this chunk is used in rollout! + """ + + def __init__(self, config: VQBeTConfig): + super().__init__() + self.config = config + + self.rgb_encoder = VQBeTRgbEncoder(config) + self.num_images = len(self.config.image_features) + # This action query token is used as a prompt for querying action chunks. Please refer to "A_Q" in the image above. + # Note: During the forward pass, this token is repeated as many times as needed. The authors also experimented with initializing the necessary number of tokens independently and observed inferior results. + self.action_token = nn.Parameter(torch.randn(1, 1, self.config.gpt_input_dim)) + + # To input state and observation features into GPT layers, we first project the features to fit the shape of input size of GPT. + self.state_projector = MLP( + config.robot_state_feature.shape[0], hidden_channels=[self.config.gpt_input_dim] + ) + self.rgb_feature_projector = MLP( + self.rgb_encoder.feature_dim, hidden_channels=[self.config.gpt_input_dim] + ) + + # GPT part of VQ-BeT + self.policy = GPT(config) + # bin prediction head / offset prediction head part of VQ-BeT + self.action_head = VQBeTHead(config) + + # Action tokens for: each observation step, the current action token, and all future action tokens. + num_tokens = self.config.n_action_pred_token + self.config.n_obs_steps - 1 + self.register_buffer( + "select_target_actions_indices", + torch.row_stack([torch.arange(i, i + self.config.action_chunk_size) for i in range(num_tokens)]), + ) + + def forward(self, batch: dict[str, Tensor], rollout: bool) -> tuple[dict, dict]: + # Input validation. + assert set(batch).issuperset({"observation.state", "observation.images"}) + batch_size, n_obs_steps = batch["observation.state"].shape[:2] + assert n_obs_steps == self.config.n_obs_steps + + # Extract image feature (first combine batch and sequence dims). + img_features = self.rgb_encoder( + einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...") + ) + # Separate batch and sequence dims. + img_features = einops.rearrange( + img_features, "(b s n) ... -> b s n ...", b=batch_size, s=n_obs_steps, n=self.num_images + ) + + # Arrange prior and current observation step tokens as shown in the class docstring. + # First project features to token dimension. + rgb_tokens = self.rgb_feature_projector( + img_features + ) # (batch, obs_step, number of different cameras, projection dims) + input_tokens = [rgb_tokens[:, :, i] for i in range(rgb_tokens.size(2))] + input_tokens.append( + self.state_projector(batch["observation.state"]) + ) # (batch, obs_step, projection dims) + input_tokens.append(einops.repeat(self.action_token, "1 1 d -> b n d", b=batch_size, n=n_obs_steps)) + # Interleave tokens by stacking and rearranging. + input_tokens = torch.stack(input_tokens, dim=2) + input_tokens = einops.rearrange(input_tokens, "b n t d -> b (n t) d") + + len_additional_action_token = self.config.n_action_pred_token - 1 + future_action_tokens = self.action_token.repeat(batch_size, len_additional_action_token, 1) + + # add additional action query tokens for predicting future action chunks + input_tokens = torch.cat([input_tokens, future_action_tokens], dim=1) + + # get action features (pass through GPT) + features = self.policy(input_tokens) + # len(self.config.input_features) is the number of different observation modes. + # this line gets the index of action prompt tokens. + historical_act_pred_index = np.arange(0, n_obs_steps) * (len(self.config.input_features) + 1) + len( + self.config.input_features + ) + + # only extract the output tokens at the position of action query: + # Behavior Transformer (BeT), and VQ-BeT are both sequence-to-sequence prediction models, + # mapping sequential observation to sequential action (please refer to section 2.2 in BeT paper https://huggingface.co/papers/2206.11251). + # Thus, it predicts a historical action sequence, in addition to current and future actions (predicting future actions : optional). + if len_additional_action_token > 0: + features = torch.cat( + [features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1 + ) + else: + features = features[:, historical_act_pred_index] + # pass through action head + action_head_output = self.action_head(features) + # if rollout, VQ-BeT don't calculate loss + if rollout: + return action_head_output["predicted_action"][:, n_obs_steps - 1, :].reshape( + batch_size, self.config.action_chunk_size, -1 + ) + # else, it calculate overall loss (bin prediction loss, and offset loss) + else: + output = batch["action"][:, self.select_target_actions_indices] + loss = self.action_head.loss_fn(action_head_output, output, reduction="mean") + return action_head_output, loss + + +class VQBeTHead(nn.Module): + def __init__(self, config: VQBeTConfig): + """ + VQBeTHead takes output of GPT layers, and pass the feature through bin prediction head (`self.map_to_cbet_preds_bin`), and offset prediction head (`self.map_to_cbet_preds_offset`) + + self.map_to_cbet_preds_bin: outputs probability of each code (for each layer). + The input dimension of `self.map_to_cbet_preds_bin` is same with the output of GPT, + and the output dimension of `self.map_to_cbet_preds_bin` is `self.vqvae_model.vqvae_num_layers (=fixed as 2) * self.config.vqvae_n_embed`. + if the agent select the code sequentially, we use self.map_to_cbet_preds_primary_bin and self.map_to_cbet_preds_secondary_bin instead of self._map_to_cbet_preds_bin. + + self.map_to_cbet_preds_offset: output the predicted offsets for all the codes in all the layers. + The input dimension of ` self.map_to_cbet_preds_offset` is same with the output of GPT, + and the output dimension of ` self.map_to_cbet_preds_offset` is `self.vqvae_model.vqvae_num_layers (=fixed as 2) * self.config.vqvae_n_embed * config.action_chunk_size * config.action_feature.shape[0]`. + """ + + super().__init__() + self.config = config + # init vqvae + self.vqvae_model = VqVae(config) + if config.sequentially_select: + self.map_to_cbet_preds_primary_bin = MLP( + in_channels=config.gpt_output_dim, + hidden_channels=[self.config.vqvae_n_embed], + ) + self.map_to_cbet_preds_secondary_bin = MLP( + in_channels=config.gpt_output_dim + self.config.vqvae_n_embed, + hidden_channels=[self.config.vqvae_n_embed], + ) + else: + self.map_to_cbet_preds_bin = MLP( + in_channels=config.gpt_output_dim, + hidden_channels=[self.vqvae_model.vqvae_num_layers * self.config.vqvae_n_embed], + ) + self.map_to_cbet_preds_offset = MLP( + in_channels=config.gpt_output_dim, + hidden_channels=[ + self.vqvae_model.vqvae_num_layers + * self.config.vqvae_n_embed + * config.action_chunk_size + * config.action_feature.shape[0], + ], + ) + # loss + self._focal_loss_fn = FocalLoss(gamma=2.0) + + def discretize(self, n_vqvae_training_steps, actions): + # Resize the action sequence data to fit the action chunk size using a sliding window approach. + actions = torch.cat( + [ + actions[:, j : j + self.config.action_chunk_size, :] + for j in range(actions.shape[1] + 1 - self.config.action_chunk_size) + ], + dim=0, + ) + # `actions` is a tensor of shape (new_batch, action_chunk_size, action_dim) where new_batch is the number of possible chunks created from the original sequences using the sliding window. + + loss, metric = self.vqvae_model.vqvae_forward(actions) + n_different_codes = sum( + [len(torch.unique(metric[2][:, i])) for i in range(self.vqvae_model.vqvae_num_layers)] + ) + n_different_combinations = len(torch.unique(metric[2], dim=0)) + recon_l1_error = metric[0].detach().cpu().item() + self.vqvae_model.optimized_steps += 1 + # if we updated RVQ more than `n_vqvae_training_steps` steps, we freeze the RVQ part. + if self.vqvae_model.optimized_steps >= n_vqvae_training_steps: + self.vqvae_model.discretized = torch.tensor(True) + self.vqvae_model.vq_layer.freeze_codebook = torch.tensor(True) + print("Finished discretizing action data!") + self.vqvae_model.eval() + for param in self.vqvae_model.vq_layer.parameters(): + param.requires_grad = False + return loss, n_different_codes, n_different_combinations, recon_l1_error + + def forward(self, x, **kwargs) -> dict: + # N is the batch size, and T is number of action query tokens, which are process through same GPT + N, T, _ = x.shape + # we calculate N and T side parallelly. Thus, the dimensions would be + # (batch size * number of action query tokens, action chunk size, action dimension) + x = einops.rearrange(x, "N T WA -> (N T) WA") + + # sample offsets + cbet_offsets = self.map_to_cbet_preds_offset(x) + cbet_offsets = einops.rearrange( + cbet_offsets, + "(NT) (G C WA) -> (NT) G C WA", + G=self.vqvae_model.vqvae_num_layers, + C=self.config.vqvae_n_embed, + ) + # if self.config.sequentially_select is True, bin prediction head first sample the primary code, and then sample secondary code + if self.config.sequentially_select: + cbet_primary_logits = self.map_to_cbet_preds_primary_bin(x) + + # select primary bin first + cbet_primary_probs = torch.softmax( + cbet_primary_logits / self.config.bet_softmax_temperature, dim=-1 + ) + NT, choices = cbet_primary_probs.shape + sampled_primary_centers = einops.rearrange( + torch.multinomial(cbet_primary_probs.view(-1, choices), num_samples=1), + "(NT) 1 -> NT", + NT=NT, + ) + + cbet_secondary_logits = self.map_to_cbet_preds_secondary_bin( + torch.cat( + (x, F.one_hot(sampled_primary_centers, num_classes=self.config.vqvae_n_embed)), + axis=1, + ) + ) + cbet_secondary_probs = torch.softmax( + cbet_secondary_logits / self.config.bet_softmax_temperature, dim=-1 + ) + sampled_secondary_centers = einops.rearrange( + torch.multinomial(cbet_secondary_probs.view(-1, choices), num_samples=1), + "(NT) 1 -> NT", + NT=NT, + ) + sampled_centers = torch.stack((sampled_primary_centers, sampled_secondary_centers), axis=1) + cbet_logits = torch.stack([cbet_primary_logits, cbet_secondary_logits], dim=1) + # if self.config.sequentially_select is False, bin prediction head samples primary and secondary code at once. + else: + cbet_logits = self.map_to_cbet_preds_bin(x) + cbet_logits = einops.rearrange( + cbet_logits, "(NT) (G C) -> (NT) G C", G=self.vqvae_model.vqvae_num_layers + ) + cbet_probs = torch.softmax(cbet_logits / self.config.bet_softmax_temperature, dim=-1) + NT, G, choices = cbet_probs.shape + sampled_centers = einops.rearrange( + torch.multinomial(cbet_probs.view(-1, choices), num_samples=1), + "(NT G) 1 -> NT G", + NT=NT, + ) + + device = get_device_from_parameters(self) + indices = ( + torch.arange(NT, device=device).unsqueeze(1), + torch.arange(self.vqvae_model.vqvae_num_layers, device=device).unsqueeze(0), + sampled_centers, + ) + # Use advanced indexing to sample the values (Extract the only offsets corresponding to the sampled codes.) + sampled_offsets = cbet_offsets[indices] + # Then, sum the offsets over the RVQ layers to get a net offset for the bin prediction + sampled_offsets = sampled_offsets.sum(dim=1) + with torch.no_grad(): + # Get the centroids (= vectors corresponding to the codes) of each layer to pass it through RVQ decoder + return_decoder_input = self.vqvae_model.get_embeddings_from_code(sampled_centers).clone().detach() + # pass the centroids through decoder to get actions. + decoded_action = self.vqvae_model.get_action_from_latent(return_decoder_input).clone().detach() + # reshaped extracted offset to match with decoded centroids + sampled_offsets = einops.rearrange( + sampled_offsets, "NT (W A) -> NT W A", W=self.config.action_chunk_size + ) + # add offset and decoded centroids + predicted_action = decoded_action + sampled_offsets + predicted_action = einops.rearrange( + predicted_action, + "(N T) W A -> N T (W A)", + N=N, + T=T, + W=self.config.action_chunk_size, + ) + + return { + "cbet_logits": cbet_logits, + "predicted_action": predicted_action, + "sampled_centers": sampled_centers, + "decoded_action": decoded_action, + } + + def loss_fn(self, pred, target, **kwargs): + """ + for given ground truth action values (target), and prediction (pred) this function calculates the overall loss. + + predicted_action: predicted action chunk (offset + decoded centroids) + sampled_centers: sampled centroids (code of RVQ) + decoded_action: decoded action, which is produced by passing sampled_centers through RVQ decoder + NT: batch size * T + T: number of action query tokens, which are process through same GPT + cbet_logits: probability of all codes in each layer + """ + action_seq = target + predicted_action = pred["predicted_action"] + sampled_centers = pred["sampled_centers"] + decoded_action = pred["decoded_action"] + NT = predicted_action.shape[0] * predicted_action.shape[1] + + cbet_logits = pred["cbet_logits"] + + predicted_action = einops.rearrange( + predicted_action, "N T (W A) -> (N T) W A", W=self.config.action_chunk_size + ) + + action_seq = einops.rearrange(action_seq, "N T W A -> (N T) W A") + # Figure out the loss for the actions. + # First, we need to find the closest cluster center for each ground truth action. + with torch.no_grad(): + state_vq, action_bins = self.vqvae_model.get_code(action_seq) # action_bins: NT, G + + # Now we can compute the loss. + + # offset loss is L1 distance between the predicted action and ground truth action + offset_loss = F.l1_loss(action_seq, predicted_action) + + # calculate primary code prediction loss + cbet_loss1 = self._focal_loss_fn( + cbet_logits[:, 0, :], + action_bins[:, 0], + ) + # calculate secondary code prediction loss + cbet_loss2 = self._focal_loss_fn( + cbet_logits[:, 1, :], + action_bins[:, 1], + ) + # add all the prediction loss + cbet_loss = ( + cbet_loss1 * self.config.primary_code_loss_weight + + cbet_loss2 * self.config.secondary_code_loss_weight + ) + + equal_primary_code_rate = torch.sum((action_bins[:, 0] == sampled_centers[:, 0]).int()) / (NT) + equal_secondary_code_rate = torch.sum((action_bins[:, 1] == sampled_centers[:, 1]).int()) / (NT) + + action_mse_error = torch.mean((action_seq - predicted_action) ** 2) + vq_action_error = torch.mean(torch.abs(action_seq - decoded_action)) + offset_action_error = torch.mean(torch.abs(action_seq - predicted_action)) + action_error_max = torch.max(torch.abs(action_seq - predicted_action)) + + loss = cbet_loss + self.config.offset_loss_weight * offset_loss + + loss_dict = { + "loss": loss, + "classification_loss": cbet_loss.detach().cpu().item(), + "offset_loss": offset_loss.detach().cpu().item(), + "equal_primary_code_rate": equal_primary_code_rate.detach().cpu().item(), + "equal_secondary_code_rate": equal_secondary_code_rate.detach().cpu().item(), + "vq_action_error": vq_action_error.detach().cpu().item(), + "offset_action_error": offset_action_error.detach().cpu().item(), + "action_error_max": action_error_max.detach().cpu().item(), + "action_mse_error": action_mse_error.detach().cpu().item(), + } + return loss_dict + + +class VQBeTRgbEncoder(nn.Module): + """Encode an RGB image into a 1D feature vector. + + Includes the ability to normalize and crop the image first. + + Same with DiffusionRgbEncoder from modeling_diffusion.py + """ + + def __init__(self, config: VQBeTConfig): + super().__init__() + # Set up optional preprocessing. + if config.crop_shape is not None: + self.do_crop = True + # Always use center crop for eval + self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape) + if config.crop_is_random: + self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape) + else: + self.maybe_random_crop = self.center_crop + else: + self.do_crop = False + + # Set up backbone. + backbone_model = getattr(torchvision.models, config.vision_backbone)( + weights=config.pretrained_backbone_weights + ) + # Note: This assumes that the layer4 feature map is children()[-3] + # TODO(alexander-soare): Use a safer alternative. + self.backbone = nn.Sequential(*(list(backbone_model.children())[:-2])) + if config.use_group_norm: + if config.pretrained_backbone_weights: + raise ValueError( + "You can't replace BatchNorm in a pretrained model without ruining the weights!" + ) + self.backbone = _replace_submodules( + root_module=self.backbone, + predicate=lambda x: isinstance(x, nn.BatchNorm2d), + func=lambda x: nn.GroupNorm(num_groups=x.num_features // 16, num_channels=x.num_features), + ) + + # Set up pooling and final layers. + # Use a dry run to get the feature map shape. + # The dummy input should take the number of image channels from `config.image_features` and it should + # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the + # height and width from `config.image_features`. + + images_shape = next(iter(config.image_features.values())).shape + dummy_shape_h_w = config.crop_shape if config.crop_shape is not None else images_shape[1:] + dummy_shape = (1, images_shape[0], *dummy_shape_h_w) + feature_map_shape = get_output_shape(self.backbone, dummy_shape)[1:] + + self.pool = SpatialSoftmax(feature_map_shape, num_kp=config.spatial_softmax_num_keypoints) + self.feature_dim = config.spatial_softmax_num_keypoints * 2 + self.out = nn.Linear(config.spatial_softmax_num_keypoints * 2, self.feature_dim) + self.relu = nn.ReLU() + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: (B, C, H, W) image tensor with pixel values in [0, 1]. + Returns: + (B, D) image feature. + """ + # Preprocess: maybe crop (if it was set up in the __init__). + if self.do_crop: + if self.training: # noqa: SIM108 + x = self.maybe_random_crop(x) + else: + # Always use center crop for eval. + x = self.center_crop(x) + # Extract backbone feature. + x = torch.flatten(self.pool(self.backbone(x)), start_dim=1) + # Final linear layer with non-linearity. + x = self.relu(self.out(x)) + return x + + +def _replace_submodules( + root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module] +) -> nn.Module: + """ + Args: + root_module: The module for which the submodules need to be replaced + predicate: Takes a module as an argument and must return True if the that module is to be replaced. + func: Takes a module as an argument and returns a new module to replace it with. + Returns: + The root module with its submodules replaced. + """ + if predicate(root_module): + return func(root_module) + + replace_list = [k.split(".") for k, m in root_module.named_modules(remove_duplicate=True) if predicate(m)] + for *parents, k in replace_list: + parent_module = root_module + if len(parents) > 0: + parent_module = root_module.get_submodule(".".join(parents)) + if isinstance(parent_module, nn.Sequential): + src_module = parent_module[int(k)] + else: + src_module = getattr(parent_module, k) + tgt_module = func(src_module) + if isinstance(parent_module, nn.Sequential): + parent_module[int(k)] = tgt_module + else: + setattr(parent_module, k, tgt_module) + # verify that all BN are replaced + assert not any(predicate(m) for _, m in root_module.named_modules(remove_duplicate=True)) + return root_module + + +class VqVae(nn.Module): + def __init__( + self, + config: VQBeTConfig, + ): + """ + VQ-VAE is composed of three parts: encoder, vq_layer, and decoder. + Encoder and decoder are MLPs consisting of an input, output layer, and hidden layer, respectively. + The vq_layer uses residual VQs. + + This class contains functions for training the encoder and decoder along with the residual VQ layer (for training phase 1), + as well as functions to help BeT training part in training phase 2. + """ + + super().__init__() + self.config = config + # 'discretized' indicates whether the Residual VQ part is trained or not. (After finishing the training, we set discretized=True) + self.register_buffer("discretized", torch.tensor(False)) + self.optimized_steps = 0 + # we use the fixed number of layers for Residual VQ across all environments. + self.vqvae_num_layers = 2 + + self.vq_layer = ResidualVQ( + dim=config.vqvae_embedding_dim, + num_quantizers=self.vqvae_num_layers, + codebook_size=config.vqvae_n_embed, + ) + + self.encoder = MLP( + in_channels=self.config.action_feature.shape[0] * self.config.action_chunk_size, + hidden_channels=[ + config.vqvae_enc_hidden_dim, + config.vqvae_enc_hidden_dim, + config.vqvae_embedding_dim, + ], + ) + self.decoder = MLP( + in_channels=config.vqvae_embedding_dim, + hidden_channels=[ + config.vqvae_enc_hidden_dim, + config.vqvae_enc_hidden_dim, + self.config.action_feature.shape[0] * self.config.action_chunk_size, + ], + ) + + def get_embeddings_from_code(self, encoding_indices): + # This function gets code indices as inputs, and outputs embedding vectors corresponding to the code indices. + with torch.no_grad(): + z_embed = self.vq_layer.get_codebook_vector_from_indices(encoding_indices) + # since the RVQ has multiple layers, it adds the vectors in the axis of layers to provide a vector for that code combination. + z_embed = z_embed.sum(dim=0) + return z_embed + + def get_action_from_latent(self, latent): + # given latent vector, this function outputs the decoded action. + output = self.decoder(latent) + if self.config.action_chunk_size == 1: + return einops.rearrange(output, "N (T A) -> N T A", A=self.config.action_feature.shape[0]) + else: + return einops.rearrange(output, "N (T A) -> N T A", A=self.config.action_feature.shape[0]) + + def get_code(self, state): + # in phase 2 of VQ-BeT training, we need a `ground truth labels of action data` to calculate the Focal loss for code prediction head. (please refer to section 3.3 in the paper https://huggingface.co/papers/2403.03181) + # this function outputs the `GT code` of given action using frozen encoder and quantization layers. (please refer to Figure 2. in the paper https://huggingface.co/papers/2403.03181) + state = einops.rearrange(state, "N T A -> N (T A)") + with torch.no_grad(): + state_rep = self.encoder(state) + state_rep_shape = state_rep.shape[:-1] + state_rep_flat = state_rep.view(state_rep.size(0), -1, state_rep.size(1)) + state_rep_flat, vq_code, vq_loss_state = self.vq_layer(state_rep_flat) + state_vq = state_rep_flat.view(*state_rep_shape, -1) + vq_code = vq_code.view(*state_rep_shape, -1) + vq_loss_state = torch.sum(vq_loss_state) + return state_vq, vq_code + + def vqvae_forward(self, state): + # This function passes the given data through Residual VQ with Encoder and Decoder. Please refer to section 3.2 in the paper https://huggingface.co/papers/2403.03181). + state = einops.rearrange(state, "N T A -> N (T A)") + # We start with passing action (or action chunk) at:t+n through the encoder ϕ. + state_rep = self.encoder(state) + state_rep_shape = state_rep.shape[:-1] + state_rep_flat = state_rep.view(state_rep.size(0), -1, state_rep.size(1)) + # The resulting latent embedding vector x = ϕ(at:t+n) is then mapped to an embedding vector in the codebook of the RVQ layers by the nearest neighbor look-up. + state_rep_flat, vq_code, vq_loss_state = self.vq_layer(state_rep_flat) + state_vq = state_rep_flat.view(*state_rep_shape, -1) + vq_code = vq_code.view(*state_rep_shape, -1) + # since the RVQ has multiple layers, it adds the vectors in the axis of layers to provide a vector for that code combination. + vq_loss_state = torch.sum(vq_loss_state) + # Then, the discretized vector zq(x) is reconstructed as ψ(zq(x)) by passing through the decoder ψ. + dec_out = self.decoder(state_vq) + # Calculate L1 reconstruction loss + encoder_loss = (state - dec_out).abs().mean() + # add encoder reconstruction loss and commitment loss + rep_loss = encoder_loss + vq_loss_state * 5 + + metric = ( + encoder_loss.clone().detach(), + vq_loss_state.clone().detach(), + vq_code, + rep_loss.item(), + ) + return rep_loss, metric + + +class FocalLoss(nn.Module): + """ + From https://github.com/notmahi/miniBET/blob/main/behavior_transformer/bet.py + """ + + def __init__(self, gamma: float = 0, size_average: bool = True): + super().__init__() + self.gamma = gamma + self.size_average = size_average + + def forward(self, input, target): + if len(input.shape) == 3: + N, T, _ = input.shape + logpt = F.log_softmax(input, dim=-1) + logpt = logpt.gather(-1, target.view(N, T, 1)).view(N, T) + elif len(input.shape) == 2: + logpt = F.log_softmax(input, dim=-1) + logpt = logpt.gather(-1, target.view(-1, 1)).view(-1) + pt = logpt.exp() + + loss = -1 * (1 - pt) ** self.gamma * logpt + if self.size_average: + return loss.mean() + else: + return loss.sum() + + +class MLP(torch.nn.Sequential): + def __init__( + self, + in_channels: int, + hidden_channels: List[int], + ): + layers = [] + in_dim = in_channels + for hidden_dim in hidden_channels[:-1]: + layers.append(torch.nn.Linear(in_dim, hidden_dim)) + layers.append(torch.nn.ReLU()) + in_dim = hidden_dim + + layers.append(torch.nn.Linear(in_dim, hidden_channels[-1])) + + super().__init__(*layers) diff --git a/lerobot/common/policies/vqbet/vqbet_utils.py b/lerobot/common/policies/vqbet/vqbet_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..37b82a53b69bc476d6b71beef7c00c7428a14da0 --- /dev/null +++ b/lerobot/common/policies/vqbet/vqbet_utils.py @@ -0,0 +1,1462 @@ +#!/usr/bin/env python + +# Copyright 2024 Seungjae Lee and Yibin Wang and Haritheja Etukuru +# and H. Jin Kim and Nur Muhammad Mahi Shafiullah and Lerrel Pinto +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from functools import partial +from math import ceil +from random import randrange +from typing import Callable + +import torch +import torch.distributed as distributed +import torch.nn.functional as F # noqa: N812 +from einops import pack, rearrange, reduce, repeat, unpack +from torch import einsum, nn +from torch.cuda.amp import autocast +from torch.optim import Optimizer + +from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig + +# ruff: noqa: N806 + +""" +This file is part of a VQ-BeT that utilizes code from the following repositories: + + - Vector Quantize PyTorch code is licensed under the MIT License: + Original source: https://github.com/lucidrains/vector-quantize-pytorch + + - nanoGPT part is an adaptation of Andrej Karpathy's nanoGPT implementation in PyTorch. + Original source: https://github.com/karpathy/nanoGPT + +We also made some changes to the original code to adapt it to our needs. The changes are described in the code below. +""" + +""" +This is a part for nanoGPT that utilizes code from the following repository: + + - Andrej Karpathy's nanoGPT implementation in PyTorch. + Original source: https://github.com/karpathy/nanoGPT + + - The nanoGPT code is licensed under the MIT License: + + MIT License + + Copyright (c) 2022 Andrej Karpathy + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + + - We've made some changes to the original code to adapt it to our needs. + + Changed variable names: + - n_head -> gpt_n_head + - n_embd -> gpt_hidden_dim + - block_size -> gpt_block_size + - n_layer -> gpt_n_layer + + + class GPT(nn.Module): + - removed unused functions `def generate`, `def estimate_mfu`, and `def from_pretrained` + - changed the `configure_optimizers` to `def configure_parameters` and made it to return only the parameters of the model: we use an external optimizer in our training loop. + - in the function `forward`, we removed target loss calculation parts, since it will be calculated in the training loop (after passing through bin prediction and offset prediction heads). + +""" + + +class CausalSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + assert config.gpt_hidden_dim % config.gpt_n_head == 0 + # key, query, value projections for all heads, but in a batch + self.c_attn = nn.Linear(config.gpt_hidden_dim, 3 * config.gpt_hidden_dim) + # output projection + self.c_proj = nn.Linear(config.gpt_hidden_dim, config.gpt_hidden_dim) + # regularization + self.attn_dropout = nn.Dropout(config.dropout) + self.resid_dropout = nn.Dropout(config.dropout) + # causal mask to ensure that attention is only applied to the left in the input sequence + self.register_buffer( + "bias", + torch.tril(torch.ones(config.gpt_block_size, config.gpt_block_size)).view( + 1, 1, config.gpt_block_size, config.gpt_block_size + ), + ) + self.gpt_n_head = config.gpt_n_head + self.gpt_hidden_dim = config.gpt_hidden_dim + + def forward(self, x): + ( + B, + T, + C, + ) = x.size() # batch size, sequence length, embedding dimensionality (gpt_hidden_dim) + + # calculate query, key, values for all heads in batch and move head forward to be the batch dim + q, k, v = self.c_attn(x).split(self.gpt_hidden_dim, dim=2) + k = k.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(1, 2) # (B, nh, T, hs) + q = q.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(1, 2) # (B, nh, T, hs) + v = v.view(B, T, self.gpt_n_head, C // self.gpt_n_head).transpose(1, 2) # (B, nh, T, hs) + + # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) + att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) + att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf")) + att = F.softmax(att, dim=-1) + att = self.attn_dropout(att) + y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs) + y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side + + # output projection + y = self.resid_dropout(self.c_proj(y)) + return y + + +class Block(nn.Module): + # causual self-attention block for GPT + def __init__(self, config): + super().__init__() + self.ln_1 = nn.LayerNorm(config.gpt_hidden_dim) + self.attn = CausalSelfAttention(config) + self.ln_2 = nn.LayerNorm(config.gpt_hidden_dim) + self.mlp = nn.Sequential( + nn.Linear(config.gpt_hidden_dim, 4 * config.gpt_hidden_dim), + nn.GELU(), + nn.Linear(4 * config.gpt_hidden_dim, config.gpt_hidden_dim), + nn.Dropout(config.dropout), + ) + + def forward(self, x): + x = x + self.attn(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class GPT(nn.Module): + """ + Original comments: + Full definition of a GPT Language Model, all of it in this single file. + References: + 1) the official GPT-2 TensorFlow implementation released by OpenAI: + https://github.com/openai/gpt-2/blob/master/src/model.py + 2) huggingface/transformers PyTorch implementation: + https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py + """ + + def __init__(self, config: VQBeTConfig): + """ + GPT model gets hyperparameters from a config object. Please refer configuration_vqbet.py for more details. + """ + super().__init__() + assert config.gpt_output_dim is not None + assert config.gpt_block_size is not None + self.config = config + + self.transformer = nn.ModuleDict( + { + "wte": nn.Linear(config.gpt_input_dim, config.gpt_hidden_dim), + "wpe": nn.Embedding(config.gpt_block_size, config.gpt_hidden_dim), + "drop": nn.Dropout(config.dropout), + "h": nn.ModuleList([Block(config) for _ in range(config.gpt_n_layer)]), + "ln_f": nn.LayerNorm(config.gpt_hidden_dim), + } + ) + self.lm_head = nn.Linear(config.gpt_hidden_dim, config.gpt_output_dim, bias=False) + # init all weights, and apply a special scaled init to the residual projections, per GPT-2 paper + self.apply(self._init_weights) + for pn, p in self.named_parameters(): + if pn.endswith("c_proj.weight"): + torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.gpt_n_layer)) + + # report number of parameters + n_params = sum(p.numel() for p in self.parameters()) + print("number of parameters: {:.2f}M".format(n_params / 1e6)) + + def forward(self, input, targets=None): + device = input.device + b, t, d = input.size() + assert t <= self.config.gpt_block_size, ( + f"Cannot forward sequence of length {t}, block size is only {self.config.gpt_block_size}" + ) + + # positional encodings that are added to the input embeddings + pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t) + + # forward the GPT model itself + tok_emb = self.transformer.wte(input) # token embeddings of shape (b, t, gpt_hidden_dim) + pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, gpt_hidden_dim) + x = self.transformer.drop(tok_emb + pos_emb) + for block in self.transformer.h: + x = block(x) + x = self.transformer.ln_f(x) + logits = self.lm_head(x) + return logits + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + elif isinstance(module, nn.LayerNorm): + torch.nn.init.zeros_(module.bias) + torch.nn.init.ones_(module.weight) + + def crop_block_size(self, gpt_block_size): + # model surgery to decrease the block size if necessary + # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024) + # but want to use a smaller block size for some smaller, simpler model + assert gpt_block_size <= self.config.gpt_block_size + self.config.gpt_block_size = gpt_block_size + self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:gpt_block_size]) + for block in self.transformer.h: + block.attn.bias = block.attn.bias[:, :, :gpt_block_size, :gpt_block_size] + + def configure_parameters(self): + """ + This long function is unfortunately doing something very simple and is being very defensive: + We are separating out all parameters of the model into two buckets: those that will experience + weight decay for regularization and those that won't (biases, and layernorm/embedding weights). + """ + + # separate out all parameters to those that will and won't experience regularizing weight decay + decay = set() + no_decay = set() + whitelist_weight_modules = (torch.nn.Linear,) + blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding) + for mn, m in self.named_modules(): + for pn, _p in m.named_parameters(): + fpn = "{}.{}".format(mn, pn) if mn else pn # full param name + if pn.endswith("bias"): + # all biases will not be decayed + no_decay.add(fpn) + elif pn.endswith("weight") and isinstance(m, whitelist_weight_modules): + # weights of whitelist modules will be weight decayed + decay.add(fpn) + elif pn.endswith("weight") and isinstance(m, blacklist_weight_modules): + # weights of blacklist modules will NOT be weight decayed + no_decay.add(fpn) + + # validate that we considered every parameter + param_dict = dict(self.named_parameters()) + inter_params = decay & no_decay + union_params = decay | no_decay + assert len(inter_params) == 0, "parameters {} made it into both decay/no_decay sets!".format( + str(inter_params) + ) + assert len(param_dict.keys() - union_params) == 0, ( + "parameters {} were not separated into either decay/no_decay set!".format( + str(param_dict.keys() - union_params), + ) + ) + + decay = [param_dict[pn] for pn in sorted(decay)] + no_decay = [param_dict[pn] for pn in sorted(no_decay)] + # return the parameters that require weight decay, and the parameters that don't separately. + return decay, no_decay + + +""" +This file is a part for Residual Vector Quantization that utilizes code from the following repository: + + - Phil Wang's vector-quantize-pytorch implementation in PyTorch. + Original source: https://github.com/lucidrains/vector-quantize-pytorch + + - The vector-quantize-pytorch code is licensed under the MIT License: + + MIT License + + Copyright (c) 2020 Phil Wang + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + + - We've made some changes to the original code to adapt it to our needs. + + class ResidualVQ(nn.Module): + - added `self.register_buffer('freeze_codebook', torch.tensor(False))` to the __init__ method: + This enables the user to save an indicator whether the codebook is frozen or not. + - changed the name of function `get_codes_from_indices` → `get_codebook_vector_from_indices`: + This is to make the function name more descriptive. + + class VectorQuantize(nn.Module): + - removed the `use_cosine_sim` and `layernorm_after_project_in` parameters from the __init__ method: + These parameters are not used in the code. + - changed the name of function `get_codes_from_indices` → `get_codebook_vector_from_indices`: + This is to make the function name more descriptive. + +""" + + +class ResidualVQ(nn.Module): + """ + Residual VQ is composed of multiple VectorQuantize layers. + + Follows Algorithm 1. in https://huggingface.co/papers/2107.03312 + "Residual Vector Quantizer (a.k.a. multi-stage vector quantizer [36]) cascades Nq layers of VQ as follows. The unquantized input vector is + passed through a first VQ and quantization residuals are computed. The residuals are then iteratively quantized by a sequence of additional + Nq -1 vector quantizers, as described in Algorithm 1." + + + self.project_in: function for projecting input to codebook dimension + self.project_out: function for projecting codebook dimension to output dimension + self.layers: nn.ModuleList of VectorQuantize layers that contains Nq layers of VQ as described in the paper. + self.freeze_codebook: buffer to save an indicator whether the codebook is frozen or not. VQ-BeT will check this to determine whether to update the codebook or not. + """ + + def __init__( + self, + *, + dim, + num_quantizers, + codebook_dim=None, + shared_codebook=False, + heads=1, + quantize_dropout=False, + quantize_dropout_cutoff_index=0, + quantize_dropout_multiple_of=1, + accept_image_fmap=False, + **kwargs, + ): + super().__init__() + assert heads == 1, "residual vq is not compatible with multi-headed codes" + codebook_dim = codebook_dim if (codebook_dim is not None) else dim + codebook_input_dim = codebook_dim * heads + + requires_projection = codebook_input_dim != dim + self.project_in = nn.Linear(dim, codebook_input_dim) if requires_projection else nn.Identity() + self.project_out = nn.Linear(codebook_input_dim, dim) if requires_projection else nn.Identity() + + self.num_quantizers = num_quantizers + + self.accept_image_fmap = accept_image_fmap + self.layers = nn.ModuleList( + [ + VectorQuantize( + dim=codebook_dim, codebook_dim=codebook_dim, accept_image_fmap=accept_image_fmap, **kwargs + ) + for _ in range(num_quantizers) + ] + ) + + self.quantize_dropout = quantize_dropout and num_quantizers > 1 + + assert quantize_dropout_cutoff_index >= 0 + + self.register_buffer("freeze_codebook", torch.tensor(False)) + self.quantize_dropout_cutoff_index = quantize_dropout_cutoff_index + self.quantize_dropout_multiple_of = quantize_dropout_multiple_of # encodec paper proposes structured dropout, believe this was set to 4 + + if not shared_codebook: + return + + first_vq, *rest_vq = self.layers + codebook = first_vq._codebook + + for vq in rest_vq: + vq._codebook = codebook + + @property + def codebooks(self): + codebooks = [layer._codebook.embed for layer in self.layers] + codebooks = torch.stack(codebooks, dim=0) + codebooks = rearrange(codebooks, "q 1 c d -> q c d") + return codebooks + + def get_codebook_vector_from_indices(self, indices): + # this function will return the codes from all codebooks across layers corresponding to the indices + batch, quantize_dim = indices.shape[0], indices.shape[-1] + + # may also receive indices in the shape of 'b h w q' (accept_image_fmap) + + indices, ps = pack([indices], "b * q") + + # because of quantize dropout, one can pass in indices that are coarse + # and the network should be able to reconstruct + + if quantize_dim < self.num_quantizers: + assert self.quantize_dropout > 0.0, ( + "quantize dropout must be greater than 0 if you wish to reconstruct from a signal with less fine quantizations" + ) + indices = F.pad(indices, (0, self.num_quantizers - quantize_dim), value=-1) + + # get ready for gathering + + codebooks = repeat(self.codebooks, "q c d -> q b c d", b=batch) + gather_indices = repeat(indices, "b n q -> q b n d", d=codebooks.shape[-1]) + + # take care of quantizer dropout + + mask = gather_indices == -1.0 + gather_indices = gather_indices.masked_fill( + mask, 0 + ) # have it fetch a dummy code to be masked out later + + all_codes = codebooks.gather(2, gather_indices) # gather all codes + + # mask out any codes that were dropout-ed + + all_codes = all_codes.masked_fill(mask, 0.0) + + # if (accept_image_fmap = True) then return shape (quantize, batch, height, width, dimension) + + (all_codes,) = unpack(all_codes, ps, "q b * d") + + return all_codes + + def forward(self, x, indices=None, return_all_codes=False, sample_codebook_temp=None): + """ + For given input tensor x, this function will return the quantized output, the indices of the quantized output, and the loss. + First, the input tensor x is projected to the codebook dimension. Then, the input tensor x is passed through Nq layers of VectorQuantize. + The residual value of each layer is fed to the next layer. + """ + num_quant, quant_dropout_multiple_of, return_loss, device = ( + self.num_quantizers, + self.quantize_dropout_multiple_of, + (indices is not None), + x.device, + ) + + x = self.project_in(x) + + assert not (self.accept_image_fmap and (indices is not None)) + + quantized_out = 0.0 + residual = x + + all_losses = [] + all_indices = [] + + if return_loss: + assert not torch.any(indices == -1), ( + "some of the residual vq indices were dropped out. please use indices derived when the module is in eval mode to derive cross entropy loss" + ) + ce_losses = [] + + should_quantize_dropout = self.training and self.quantize_dropout and not return_loss + + # sample a layer index at which to dropout further residual quantization + # also prepare null indices and loss + + if should_quantize_dropout: + rand_quantize_dropout_index = randrange(self.quantize_dropout_cutoff_index, num_quant) + + if quant_dropout_multiple_of != 1: + rand_quantize_dropout_index = ( + ceil((rand_quantize_dropout_index + 1) / quant_dropout_multiple_of) + * quant_dropout_multiple_of + - 1 + ) + + null_indices_shape = (x.shape[0], *x.shape[-2:]) if self.accept_image_fmap else tuple(x.shape[:2]) + null_indices = torch.full(null_indices_shape, -1.0, device=device, dtype=torch.long) + null_loss = torch.full((1,), 0.0, device=device, dtype=x.dtype) + + # go through the layers + + for quantizer_index, layer in enumerate(self.layers): + if should_quantize_dropout and quantizer_index > rand_quantize_dropout_index: + all_indices.append(null_indices) + all_losses.append(null_loss) + continue + + layer_indices = None + if return_loss: + layer_indices = indices[..., quantizer_index] + + quantized, *rest = layer( + residual, + indices=layer_indices, + sample_codebook_temp=sample_codebook_temp, + freeze_codebook=self.freeze_codebook, + ) + + residual = residual - quantized.detach() + quantized_out = quantized_out + quantized + + if return_loss: + ce_loss = rest[0] + ce_losses.append(ce_loss) + continue + + embed_indices, loss = rest + + all_indices.append(embed_indices) + all_losses.append(loss) + + # project out, if needed + + quantized_out = self.project_out(quantized_out) + + # whether to early return the cross entropy loss + + if return_loss: + return quantized_out, sum(ce_losses) + + # stack all losses and indices + + all_losses, all_indices = map(partial(torch.stack, dim=-1), (all_losses, all_indices)) + + ret = (quantized_out, all_indices, all_losses) + + if return_all_codes: + # whether to return all codes from all codebooks across layers + all_codes = self.get_codebook_vector_from_indices(all_indices) + + # will return all codes in shape (quantizer, batch, sequence length, codebook dimension) + ret = (*ret, all_codes) + + return ret + + +class VectorQuantize(nn.Module): + def __init__( + self, + dim, + codebook_size, + codebook_dim=None, + heads=1, + separate_codebook_per_head=False, + decay=0.8, + eps=1e-5, + kmeans_init=False, + kmeans_iters=10, + sync_kmeans=True, + threshold_ema_dead_code=0, + channel_last=True, + accept_image_fmap=False, + commitment_weight=1.0, + commitment_use_cross_entropy_loss=False, + orthogonal_reg_weight=0.0, + orthogonal_reg_active_codes_only=False, + orthogonal_reg_max_codes=None, + stochastic_sample_codes=False, + sample_codebook_temp=1.0, + straight_through=False, + reinmax=False, # using reinmax for improved straight-through, assuming straight through helps at all + sync_codebook=None, + sync_affine_param=False, + ema_update=True, + learnable_codebook=False, + in_place_codebook_optimizer: Callable[ + ..., Optimizer + ] = None, # Optimizer used to update the codebook embedding if using learnable_codebook + affine_param=False, + affine_param_batch_decay=0.99, + affine_param_codebook_decay=0.9, + sync_update_v=0.0, # the v that controls optimistic vs pessimistic update for synchronous update rule (21) https://minyoungg.github.io/vqtorch/assets/draft_050523.pdf + ): + super().__init__() + self.dim = dim + self.heads = heads + self.separate_codebook_per_head = separate_codebook_per_head + + codebook_dim = codebook_dim if (codebook_dim is not None) else dim + codebook_input_dim = codebook_dim * heads + + requires_projection = codebook_input_dim != dim + self.project_in = nn.Linear(dim, codebook_input_dim) if requires_projection else nn.Identity() + self.project_out = nn.Linear(codebook_input_dim, dim) if requires_projection else nn.Identity() + + self.eps = eps + self.commitment_weight = commitment_weight + self.commitment_use_cross_entropy_loss = commitment_use_cross_entropy_loss # whether to use cross entropy loss to codebook as commitment loss + + self.learnable_codebook = learnable_codebook + + has_codebook_orthogonal_loss = orthogonal_reg_weight > 0 + self.has_codebook_orthogonal_loss = has_codebook_orthogonal_loss + self.orthogonal_reg_weight = orthogonal_reg_weight + self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only + self.orthogonal_reg_max_codes = orthogonal_reg_max_codes + + assert not (ema_update and learnable_codebook), "learnable codebook not compatible with EMA update" + + assert 0 <= sync_update_v <= 1.0 + assert not (sync_update_v > 0.0 and not learnable_codebook), "learnable codebook must be turned on" + + self.sync_update_v = sync_update_v + + gumbel_sample_fn = partial( + gumbel_sample, + stochastic=stochastic_sample_codes, + reinmax=reinmax, + straight_through=straight_through, + ) + + if sync_codebook is None: + sync_codebook = distributed.is_initialized() and distributed.get_world_size() > 1 + + codebook_kwargs = { + "dim": codebook_dim, + "num_codebooks": heads if separate_codebook_per_head else 1, + "codebook_size": codebook_size, + "kmeans_init": kmeans_init, + "kmeans_iters": kmeans_iters, + "sync_kmeans": sync_kmeans, + "decay": decay, + "eps": eps, + "threshold_ema_dead_code": threshold_ema_dead_code, + "use_ddp": sync_codebook, + "learnable_codebook": has_codebook_orthogonal_loss or learnable_codebook, + "sample_codebook_temp": sample_codebook_temp, + "gumbel_sample": gumbel_sample_fn, + "ema_update": ema_update, + } + + if affine_param: + codebook_kwargs = dict( + **codebook_kwargs, + affine_param=True, + sync_affine_param=sync_affine_param, + affine_param_batch_decay=affine_param_batch_decay, + affine_param_codebook_decay=affine_param_codebook_decay, + ) + + self._codebook = EuclideanCodebook(**codebook_kwargs) + + self.in_place_codebook_optimizer = ( + in_place_codebook_optimizer(self._codebook.parameters()) + if (in_place_codebook_optimizer is not None) + else None + ) + + self.codebook_size = codebook_size + + self.accept_image_fmap = accept_image_fmap + self.channel_last = channel_last + + @property + def codebook(self): + codebook = self._codebook.embed + + if self.separate_codebook_per_head: + return codebook + + return rearrange(codebook, "1 ... -> ...") + + @codebook.setter + def codebook(self, codes): + if not self.separate_codebook_per_head: + codes = rearrange(codes, "... -> 1 ...") + + self._codebook.embed.copy_(codes) + + def get_codebook_vector_from_indices(self, indices): + codebook = self.codebook + is_multiheaded = codebook.ndim > 2 + + if not is_multiheaded: + codes = codebook[indices] + return rearrange(codes, "... h d -> ... (h d)") + + indices, ps = pack_one(indices, "b * h") + indices = rearrange(indices, "b n h -> b h n") + + indices = repeat(indices, "b h n -> b h n d", d=codebook.shape[-1]) + codebook = repeat(codebook, "h n d -> b h n d", b=indices.shape[0]) + + codes = codebook.gather(2, indices) + codes = rearrange(codes, "b h n d -> b n (h d)") + codes = unpack_one(codes, ps, "b * d") + return codes + + def forward( + self, + x, + indices=None, + mask=None, + sample_codebook_temp=None, + freeze_codebook=False, + ): + orig_input = x + + only_one = x.ndim == 2 + + if only_one: + assert mask is None + x = rearrange(x, "b d -> b 1 d") + + shape, device, heads, is_multiheaded, _codebook_size, return_loss = ( + x.shape, + x.device, + self.heads, + self.heads > 1, + self.codebook_size, + (indices is not None), + ) + + need_transpose = not self.channel_last and not self.accept_image_fmap + should_inplace_optimize = self.in_place_codebook_optimizer is not None + + # rearrange inputs + + if self.accept_image_fmap: + height, width = x.shape[-2:] + x = rearrange(x, "b c h w -> b (h w) c") + + if need_transpose: + x = rearrange(x, "b d n -> b n d") + + # project input + + x = self.project_in(x) + + # handle multi-headed separate codebooks + + if is_multiheaded: + ein_rhs_eq = "h b n d" if self.separate_codebook_per_head else "1 (b h) n d" + x = rearrange(x, f"b n (h d) -> {ein_rhs_eq}", h=heads) + + # l2norm for cosine sim, otherwise identity + + x = self._codebook.transform_input(x) + + # codebook forward kwargs + + codebook_forward_kwargs = { + "sample_codebook_temp": sample_codebook_temp, + "mask": mask, + "freeze_codebook": freeze_codebook, + } + + # quantize + + quantize, embed_ind, distances = self._codebook(x, **codebook_forward_kwargs) + + # one step in-place update + + if should_inplace_optimize and self.training and not freeze_codebook: + if mask is not None: + loss = F.mse_loss(quantize, x.detach(), reduction="none") + + loss_mask = mask + if is_multiheaded: + loss_mask = repeat( + mask, + "b n -> c (b h) n", + c=loss.shape[0], + h=loss.shape[1] // mask.shape[0], + ) + + loss = loss[loss_mask].mean() + + else: + loss = F.mse_loss(quantize, x.detach()) + + loss.backward() + self.in_place_codebook_optimizer.step() + self.in_place_codebook_optimizer.zero_grad() + + # quantize again + + quantize, embed_ind, distances = self._codebook(x, **codebook_forward_kwargs) + + if self.training: + # determine code to use for commitment loss + maybe_detach = torch.detach if not self.learnable_codebook or freeze_codebook else identity + + commit_quantize = maybe_detach(quantize) + + # straight through + + quantize = x + (quantize - x).detach() + + if self.sync_update_v > 0.0: + # (21) in https://minyoungg.github.io/vqtorch/assets/draft_050523.pdf + quantize = quantize + self.sync_update_v * (quantize - quantize.detach()) + + # function for calculating cross entropy loss to distance matrix + # used for (1) naturalspeech2 training residual vq latents to be close to the correct codes and (2) cross-entropy based commitment loss + + def calculate_ce_loss(codes): + if not is_multiheaded: + dist_einops_eq = "1 b n l -> b l n" + elif self.separate_codebook_per_head: + dist_einops_eq = "c b n l -> b l n c" + else: + dist_einops_eq = "1 (b h) n l -> b l n h" + + ce_loss = F.cross_entropy( + rearrange(distances, dist_einops_eq, b=shape[0]), codes, ignore_index=-1 + ) + + return ce_loss + + # if returning cross entropy loss on codes that were passed in + + if return_loss: + return quantize, calculate_ce_loss(indices) + + # transform embedding indices + + if is_multiheaded: + if self.separate_codebook_per_head: + embed_ind = rearrange(embed_ind, "h b n -> b n h", h=heads) + else: + embed_ind = rearrange(embed_ind, "1 (b h) n -> b n h", h=heads) + + if self.accept_image_fmap: + embed_ind = rearrange(embed_ind, "b (h w) ... -> b h w ...", h=height, w=width) + + if only_one: + embed_ind = rearrange(embed_ind, "b 1 -> b") + + # aggregate loss + + loss = torch.tensor([0.0], device=device, requires_grad=self.training) + + if self.training: + if self.commitment_weight > 0: + if self.commitment_use_cross_entropy_loss: + if mask is not None: + ce_loss_mask = mask + if is_multiheaded: + ce_loss_mask = repeat(ce_loss_mask, "b n -> b n h", h=heads) + + embed_ind.masked_fill_(~ce_loss_mask, -1) + + commit_loss = calculate_ce_loss(embed_ind) + else: + if mask is not None: + # with variable lengthed sequences + commit_loss = F.mse_loss(commit_quantize, x, reduction="none") + + loss_mask = mask + if is_multiheaded: + loss_mask = repeat( + loss_mask, + "b n -> c (b h) n", + c=commit_loss.shape[0], + h=commit_loss.shape[1] // mask.shape[0], + ) + + commit_loss = commit_loss[loss_mask].mean() + else: + commit_loss = F.mse_loss(commit_quantize, x) + + loss = loss + commit_loss * self.commitment_weight + + if self.has_codebook_orthogonal_loss: + codebook = self._codebook.embed + + # only calculate orthogonal loss for the activated codes for this batch + + if self.orthogonal_reg_active_codes_only: + assert not (is_multiheaded and self.separate_codebook_per_head), ( + "orthogonal regularization for only active codes not compatible with multi-headed with separate codebooks yet" + ) + unique_code_ids = torch.unique(embed_ind) + codebook = codebook[:, unique_code_ids] + + num_codes = codebook.shape[-2] + + if (self.orthogonal_reg_max_codes is not None) and num_codes > self.orthogonal_reg_max_codes: + rand_ids = torch.randperm(num_codes, device=device)[: self.orthogonal_reg_max_codes] + codebook = codebook[:, rand_ids] + + orthogonal_reg_loss = orthogonal_loss_fn(codebook) + loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight + + # handle multi-headed quantized embeddings + + if is_multiheaded: + if self.separate_codebook_per_head: + quantize = rearrange(quantize, "h b n d -> b n (h d)", h=heads) + else: + quantize = rearrange(quantize, "1 (b h) n d -> b n (h d)", h=heads) + + # project out + + quantize = self.project_out(quantize) + + # rearrange quantized embeddings + + if need_transpose: + quantize = rearrange(quantize, "b n d -> b d n") + + if self.accept_image_fmap: + quantize = rearrange(quantize, "b (h w) c -> b c h w", h=height, w=width) + + if only_one: + quantize = rearrange(quantize, "b 1 d -> b d") + + # if masking, only return quantized for where mask has True + + if mask is not None: + quantize = torch.where(rearrange(mask, "... -> ... 1"), quantize, orig_input) + + return quantize, embed_ind, loss + + +def noop(*args, **kwargs): + pass + + +def identity(t): + return t + + +def cdist(x, y): + x2 = reduce(x**2, "b n d -> b n", "sum") + y2 = reduce(y**2, "b n d -> b n", "sum") + xy = einsum("b i d, b j d -> b i j", x, y) * -2 + return (rearrange(x2, "b i -> b i 1") + rearrange(y2, "b j -> b 1 j") + xy).sqrt() + + +def log(t, eps=1e-20): + return torch.log(t.clamp(min=eps)) + + +def ema_inplace(old, new, decay): + is_mps = str(old.device).startswith("mps:") + + if not is_mps: + old.lerp_(new, 1 - decay) + else: + old.mul_(decay).add_(new * (1 - decay)) + + +def pack_one(t, pattern): + return pack([t], pattern) + + +def unpack_one(t, ps, pattern): + return unpack(t, ps, pattern)[0] + + +def uniform_init(*shape): + t = torch.empty(shape) + nn.init.kaiming_uniform_(t) + return t + + +def gumbel_noise(t): + noise = torch.zeros_like(t).uniform_(0, 1) + return -log(-log(noise)) + + +def gumbel_sample( + logits, + temperature=1.0, + stochastic=False, + straight_through=False, + reinmax=False, + dim=-1, + training=True, +): + dtype, size = logits.dtype, logits.shape[dim] + + if training and stochastic and temperature > 0: + sampling_logits = (logits / temperature) + gumbel_noise(logits) + else: + sampling_logits = logits + + ind = sampling_logits.argmax(dim=dim) + one_hot = F.one_hot(ind, size).type(dtype) + + assert not (reinmax and not straight_through), ( + "reinmax can only be turned on if using straight through gumbel softmax" + ) + + if not straight_through or temperature <= 0.0 or not training: + return ind, one_hot + + # use reinmax for better second-order accuracy - https://huggingface.co/papers/2304.08612 + # algorithm 2 + + if reinmax: + π0 = logits.softmax(dim=dim) + π1 = (one_hot + (logits / temperature).softmax(dim=dim)) / 2 + π1 = ((log(π1) - logits).detach() + logits).softmax(dim=1) + π2 = 2 * π1 - 0.5 * π0 + one_hot = π2 - π2.detach() + one_hot + else: + π1 = (logits / temperature).softmax(dim=dim) + one_hot = one_hot + π1 - π1.detach() + + return ind, one_hot + + +def laplace_smoothing(x, n_categories, eps=1e-5, dim=-1): + denom = x.sum(dim=dim, keepdim=True) + return (x + eps) / (denom + n_categories * eps) + + +def sample_vectors(samples, num): + num_samples, device = samples.shape[0], samples.device + if num_samples >= num: + indices = torch.randperm(num_samples, device=device)[:num] + else: + indices = torch.randint(0, num_samples, (num,), device=device) + + return samples[indices] + + +def batched_sample_vectors(samples, num): + return torch.stack([sample_vectors(sample, num) for sample in samples.unbind(dim=0)], dim=0) + + +def pad_shape(shape, size, dim=0): + return [size if i == dim else s for i, s in enumerate(shape)] + + +def sample_multinomial(total_count, probs): + device = probs.device + probs = probs.cpu() + + total_count = probs.new_full((), total_count) + remainder = probs.new_ones(()) + sample = torch.empty_like(probs, dtype=torch.long) + + for i, p in enumerate(probs): + s = torch.binomial(total_count, p / remainder) + sample[i] = s + total_count -= s + remainder -= p + + return sample.to(device) + + +def all_gather_sizes(x, dim): + size = torch.tensor(x.shape[dim], dtype=torch.long, device=x.device) + all_sizes = [torch.empty_like(size) for _ in range(distributed.get_world_size())] + distributed.all_gather(all_sizes, size) + return torch.stack(all_sizes) + + +def all_gather_variably_sized(x, sizes, dim=0): + rank = distributed.get_rank() + all_x = [] + + for i, size in enumerate(sizes): + t = x if i == rank else x.new_empty(pad_shape(x.shape, size, dim)) + distributed.broadcast(t, src=i, async_op=True) + all_x.append(t) + + distributed.barrier() + return all_x + + +def sample_vectors_distributed(local_samples, num): + local_samples = rearrange(local_samples, "1 ... -> ...") + + rank = distributed.get_rank() + all_num_samples = all_gather_sizes(local_samples, dim=0) + + if rank == 0: + samples_per_rank = sample_multinomial(num, all_num_samples / all_num_samples.sum()) + else: + samples_per_rank = torch.empty_like(all_num_samples) + + distributed.broadcast(samples_per_rank, src=0) + samples_per_rank = samples_per_rank.tolist() + + local_samples = sample_vectors(local_samples, samples_per_rank[rank]) + all_samples = all_gather_variably_sized(local_samples, samples_per_rank, dim=0) + out = torch.cat(all_samples, dim=0) + + return rearrange(out, "... -> 1 ...") + + +def batched_bincount(x, *, minlength): + batch, dtype, device = x.shape[0], x.dtype, x.device + target = torch.zeros(batch, minlength, dtype=dtype, device=device) + values = torch.ones_like(x) + target.scatter_add_(-1, x, values) + return target + + +def kmeans( + samples, + num_clusters, + num_iters=10, + sample_fn=batched_sample_vectors, + all_reduce_fn=noop, +): + num_codebooks, dim, dtype, _device = ( + samples.shape[0], + samples.shape[-1], + samples.dtype, + samples.device, + ) + + means = sample_fn(samples, num_clusters) + + for _ in range(num_iters): + dists = -torch.cdist(samples, means, p=2) + + buckets = torch.argmax(dists, dim=-1) + bins = batched_bincount(buckets, minlength=num_clusters) + all_reduce_fn(bins) + + zero_mask = bins == 0 + bins_min_clamped = bins.masked_fill(zero_mask, 1) + + new_means = buckets.new_zeros(num_codebooks, num_clusters, dim, dtype=dtype) + + new_means.scatter_add_(1, repeat(buckets, "h n -> h n d", d=dim), samples) + new_means = new_means / rearrange(bins_min_clamped, "... -> ... 1") + all_reduce_fn(new_means) + + means = torch.where(rearrange(zero_mask, "... -> ... 1"), means, new_means) + + return means, bins + + +def batched_embedding(indices, embeds): + batch, dim = indices.shape[1], embeds.shape[-1] + indices = repeat(indices, "h b n -> h b n d", d=dim) + embeds = repeat(embeds, "h c d -> h b c d", b=batch) + return embeds.gather(2, indices) + + +def orthogonal_loss_fn(t): + # eq (2) from https://huggingface.co/papers/2112.00384 + h, n = t.shape[:2] + normed_codes = F.normalize(t, p=2, dim=-1) + cosine_sim = einsum("h i d, h j d -> h i j", normed_codes, normed_codes) + return (cosine_sim**2).sum() / (h * n**2) - (1 / n) + + +class EuclideanCodebook(nn.Module): + def __init__( + self, + dim, + codebook_size, + num_codebooks=1, + kmeans_init=False, + kmeans_iters=10, + sync_kmeans=True, + decay=0.8, + eps=1e-5, + threshold_ema_dead_code=2, + reset_cluster_size=None, + use_ddp=False, + learnable_codebook=False, + gumbel_sample=gumbel_sample, + sample_codebook_temp=1.0, + ema_update=True, + affine_param=False, + sync_affine_param=False, + affine_param_batch_decay=0.99, + affine_param_codebook_decay=0.9, + ): + super().__init__() + self.transform_input = identity + + self.decay = decay + self.ema_update = ema_update + + init_fn = uniform_init if not kmeans_init else torch.zeros + embed = init_fn(num_codebooks, codebook_size, dim) + + self.codebook_size = codebook_size + self.num_codebooks = num_codebooks + + self.kmeans_iters = kmeans_iters + self.eps = eps + self.threshold_ema_dead_code = threshold_ema_dead_code + self.reset_cluster_size = ( + reset_cluster_size if (reset_cluster_size is not None) else threshold_ema_dead_code + ) + + assert callable(gumbel_sample) + self.gumbel_sample = gumbel_sample + self.sample_codebook_temp = sample_codebook_temp + + assert not (use_ddp and num_codebooks > 1 and kmeans_init), ( + "kmeans init is not compatible with multiple codebooks in distributed environment for now" + ) + + self.sample_fn = sample_vectors_distributed if use_ddp and sync_kmeans else batched_sample_vectors + self.kmeans_all_reduce_fn = distributed.all_reduce if use_ddp and sync_kmeans else noop + self.all_reduce_fn = distributed.all_reduce if use_ddp else noop + + self.register_buffer("initted", torch.Tensor([not kmeans_init])) + self.register_buffer("cluster_size", torch.zeros(num_codebooks, codebook_size)) + self.register_buffer("embed_avg", embed.clone()) + + self.learnable_codebook = learnable_codebook + if learnable_codebook: + self.embed = nn.Parameter(embed) + else: + self.register_buffer("embed", embed) + + # affine related params + + self.affine_param = affine_param + self.sync_affine_param = sync_affine_param + + if not affine_param: + return + + self.affine_param_batch_decay = affine_param_batch_decay + self.affine_param_codebook_decay = affine_param_codebook_decay + + self.register_buffer("batch_mean", None) + self.register_buffer("batch_variance", None) + + self.register_buffer("codebook_mean_needs_init", torch.Tensor([True])) + self.register_buffer("codebook_mean", torch.empty(num_codebooks, 1, dim)) + self.register_buffer("codebook_variance_needs_init", torch.Tensor([True])) + self.register_buffer("codebook_variance", torch.empty(num_codebooks, 1, dim)) + + @torch.jit.ignore + def init_embed_(self, data, mask=None): + if self.initted: + return + + if mask is not None: + c = data.shape[0] + data = rearrange(data[mask], "(c n) d -> c n d", c=c) + + embed, cluster_size = kmeans( + data, + self.codebook_size, + self.kmeans_iters, + sample_fn=self.sample_fn, + all_reduce_fn=self.kmeans_all_reduce_fn, + ) + + embed_sum = embed * rearrange(cluster_size, "... -> ... 1") + + self.embed.data.copy_(embed) + self.embed_avg.data.copy_(embed_sum) + self.cluster_size.data.copy_(cluster_size) + self.initted.data.copy_(torch.Tensor([True])) + + @torch.jit.ignore + def update_with_decay(self, buffer_name, new_value, decay): + old_value = getattr(self, buffer_name) + + needs_init = getattr(self, buffer_name + "_needs_init", False) + + if needs_init: + self.register_buffer(buffer_name + "_needs_init", torch.Tensor([False])) + + if not (old_value is not None) or needs_init: + self.register_buffer(buffer_name, new_value.detach()) + + return + + value = old_value * decay + new_value.detach() * (1 - decay) + self.register_buffer(buffer_name, value) + + @torch.jit.ignore + def update_affine(self, data, embed, mask=None): + assert self.affine_param + + var_fn = partial(torch.var, unbiased=False) + + # calculate codebook mean and variance + + embed = rearrange(embed, "h ... d -> h (...) d") + + if self.training: + self.update_with_decay( + "codebook_mean", + reduce(embed, "h n d -> h 1 d", "mean"), + self.affine_param_codebook_decay, + ) + self.update_with_decay( + "codebook_variance", + reduce(embed, "h n d -> h 1 d", var_fn), + self.affine_param_codebook_decay, + ) + + # prepare batch data, which depends on whether it has masking + + data = rearrange(data, "h ... d -> h (...) d") + + if mask is not None: + c = data.shape[0] + data = rearrange(data[mask], "(c n) d -> c n d", c=c) + + # calculate batch mean and variance + + if not self.sync_affine_param: + self.update_with_decay( + "batch_mean", + reduce(data, "h n d -> h 1 d", "mean"), + self.affine_param_batch_decay, + ) + self.update_with_decay( + "batch_variance", + reduce(data, "h n d -> h 1 d", var_fn), + self.affine_param_batch_decay, + ) + return + + num_vectors, device, dtype = data.shape[-2], data.device, data.dtype + + # number of vectors, for denominator + + num_vectors = torch.tensor([num_vectors], device=device, dtype=dtype) + distributed.all_reduce(num_vectors) + + # calculate distributed mean + + batch_sum = reduce(data, "h n d -> h 1 d", "sum") + distributed.all_reduce(batch_sum) + batch_mean = batch_sum / num_vectors + + self.update_with_decay("batch_mean", batch_mean, self.affine_param_batch_decay) + + # calculate distributed variance + + variance_number = reduce((data - batch_mean) ** 2, "h n d -> h 1 d", "sum") + distributed.all_reduce(variance_number) + batch_variance = variance_number / num_vectors + + self.update_with_decay("batch_variance", batch_variance, self.affine_param_batch_decay) + + def replace(self, batch_samples, batch_mask): + for ind, (samples, mask) in enumerate( + zip(batch_samples.unbind(dim=0), batch_mask.unbind(dim=0), strict=False) + ): + if not torch.any(mask): + continue + + sampled = self.sample_fn(rearrange(samples, "... -> 1 ..."), mask.sum().item()) + sampled = rearrange(sampled, "1 ... -> ...") + + self.embed.data[ind][mask] = sampled + + self.cluster_size.data[ind][mask] = self.reset_cluster_size + self.embed_avg.data[ind][mask] = sampled * self.reset_cluster_size + + def expire_codes_(self, batch_samples): + if self.threshold_ema_dead_code == 0: + return + + expired_codes = self.cluster_size < self.threshold_ema_dead_code + + if not torch.any(expired_codes): + return + + batch_samples = rearrange(batch_samples, "h ... d -> h (...) d") + self.replace(batch_samples, batch_mask=expired_codes) + + @autocast(enabled=False) + def forward(self, x, sample_codebook_temp=None, mask=None, freeze_codebook=False): + needs_codebook_dim = x.ndim < 4 + sample_codebook_temp = ( + sample_codebook_temp if (sample_codebook_temp is not None) else self.sample_codebook_temp + ) + + x = x.float() + + if needs_codebook_dim: + x = rearrange(x, "... -> 1 ...") + + flatten, ps = pack_one(x, "h * d") + + if mask is not None: + mask = repeat( + mask, + "b n -> c (b h n)", + c=flatten.shape[0], + h=flatten.shape[-2] // (mask.shape[0] * mask.shape[1]), + ) + + self.init_embed_(flatten, mask=mask) + + if self.affine_param: + self.update_affine(flatten, self.embed, mask=mask) + + embed = self.embed if self.learnable_codebook else self.embed.detach() + + if self.affine_param: + codebook_std = self.codebook_variance.clamp(min=1e-5).sqrt() + batch_std = self.batch_variance.clamp(min=1e-5).sqrt() + embed = (embed - self.codebook_mean) * (batch_std / codebook_std) + self.batch_mean + + dist = -cdist(flatten, embed) + + embed_ind, embed_onehot = self.gumbel_sample( + dist, dim=-1, temperature=sample_codebook_temp, training=self.training + ) + + embed_ind = unpack_one(embed_ind, ps, "h *") + + if self.training: + unpacked_onehot = unpack_one(embed_onehot, ps, "h * c") + quantize = einsum("h b n c, h c d -> h b n d", unpacked_onehot, embed) + else: + quantize = batched_embedding(embed_ind, embed) + + if self.training and self.ema_update and not freeze_codebook: + if self.affine_param: + flatten = (flatten - self.batch_mean) * (codebook_std / batch_std) + self.codebook_mean + + if mask is not None: + embed_onehot[~mask] = 0.0 + + cluster_size = embed_onehot.sum(dim=1) + + self.all_reduce_fn(cluster_size) + ema_inplace(self.cluster_size.data, cluster_size, self.decay) + + embed_sum = einsum("h n d, h n c -> h c d", flatten, embed_onehot) + self.all_reduce_fn(embed_sum.contiguous()) + ema_inplace(self.embed_avg.data, embed_sum, self.decay) + + cluster_size = laplace_smoothing( + self.cluster_size, self.codebook_size, self.eps + ) * self.cluster_size.sum(dim=-1, keepdim=True) + + embed_normalized = self.embed_avg / rearrange(cluster_size, "... -> ... 1") + self.embed.data.copy_(embed_normalized) + self.expire_codes_(x) + + if needs_codebook_dim: + quantize, embed_ind = tuple(rearrange(t, "1 ... -> ...") for t in (quantize, embed_ind)) + + dist = unpack_one(dist, ps, "h * d") + + return quantize, embed_ind, dist diff --git a/lerobot/common/robots/__init__.py b/lerobot/common/robots/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9d8b1e05178329a9cc62e2d76d8db44caff6913e --- /dev/null +++ b/lerobot/common/robots/__init__.py @@ -0,0 +1,3 @@ +from .config import RobotConfig +from .robot import Robot +from .utils import make_robot_from_config diff --git a/lerobot/common/robots/config.py b/lerobot/common/robots/config.py new file mode 100644 index 0000000000000000000000000000000000000000..245ee1f9631269c8ee306d399e77ddfe6b4b641c --- /dev/null +++ b/lerobot/common/robots/config.py @@ -0,0 +1,40 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from dataclasses import dataclass +from pathlib import Path + +import draccus + + +@dataclass(kw_only=True) +class RobotConfig(draccus.ChoiceRegistry, abc.ABC): + # Allows to distinguish between different robots of the same type + id: str | None = None + # Directory to store calibration file + calibration_dir: Path | None = None + + def __post_init__(self): + if hasattr(self, "cameras") and self.cameras: + for _, config in self.cameras.items(): + for attr in ["width", "height", "fps"]: + if getattr(config, attr) is None: + raise ValueError( + f"Specifying '{attr}' is required for the camera to be used in a robot" + ) + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) diff --git a/lerobot/common/robots/koch_follower/__init__.py b/lerobot/common/robots/koch_follower/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..43da48151f1134d5470188291008363891f89caa --- /dev/null +++ b/lerobot/common/robots/koch_follower/__init__.py @@ -0,0 +1,2 @@ +from .config_koch_follower import KochFollowerConfig +from .koch_follower import KochFollower diff --git a/lerobot/common/robots/koch_follower/config_koch_follower.py b/lerobot/common/robots/koch_follower/config_koch_follower.py new file mode 100644 index 0000000000000000000000000000000000000000..00300629c1e32d78f612f6dd636e321623ce0019 --- /dev/null +++ b/lerobot/common/robots/koch_follower/config_koch_follower.py @@ -0,0 +1,39 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.cameras import CameraConfig + +from ..config import RobotConfig + + +@RobotConfig.register_subclass("koch_follower") +@dataclass +class KochFollowerConfig(RobotConfig): + # Port to connect to the arm + port: str + + disable_torque_on_disconnect: bool = True + + # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. + # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as + # the number of motors in your follower arms. + max_relative_target: int | None = None + + # cameras + cameras: dict[str, CameraConfig] = field(default_factory=dict) + + # Set to `True` for backward compatibility with previous policies/dataset + use_degrees: bool = False diff --git a/lerobot/common/robots/koch_follower/koch.mdx b/lerobot/common/robots/koch_follower/koch.mdx new file mode 100644 index 0000000000000000000000000000000000000000..82d56c088f29c95a7192159fbd77ab742e16366c --- /dev/null +++ b/lerobot/common/robots/koch_follower/koch.mdx @@ -0,0 +1,258 @@ +# Koch v1.1 + +In the steps below, we explain how to assemble the Koch v1.1 robot. + +## Order and assemble the parts + +Follow the sourcing and assembling instructions provided in this [README](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below. + +For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk). + +> [!WARNING] +> Since the production of this video, we simplified the configuration phase. Because of this, two things differ from the instructions in that video: +> - Don't plug in all the motor cables right away and wait to be instructed to do so in [Configure the motors](#configure-the-motors). +> - Don't screw in the controller board (PCB) to the base right away and wait for being instructed to do so in [Configure the motors](#configure-the-motors). + + +## Install LeRobot 🤗 + +To install LeRobot follow, our [Installation Guide](./installation) + +In addition to these instructions, you need to install the Dynamixel SDK: +```bash +pip install -e ".[dynamixel]" +``` + +## Configure the motors + +### 1. Find the USB ports associated with each arm + +To find the port for each bus servo adapter, run this script: +```bash +python lerobot/find_port.py +``` + + + + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751'] +Remove the USB cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/tty.usbmodem575E0032081 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm. + + + + +On Linux, you might need to give access to the USB ports by running: +```bash +sudo chmod 666 /dev/ttyACM0 +sudo chmod 666 /dev/ttyACM1 +``` + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/ttyACM0', '/dev/ttyACM1'] +Remove the usb cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/ttyACM1 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm. + + + + +### 2. Set the motors ids and baudrates + +Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate. + +To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once. + +If you are repurposing motors from another robot, you will probably also need to perform this step, as the ids and baudrate likely won't match. + +#### Follower + +Connect the usb cable from your computer and the 5V power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter. + +For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm. + + + + +```bash +python -m lerobot.setup_motors \ + --robot.type=koch_follower \ + --robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step +``` + + + +```python +from lerobot.common.robots.koch_follower import KochFollower, KochFollowerConfig + +config = KochFollowerConfig( + port="/dev/tty.usbmodem575E0031751", + id="my_awesome_follower_arm", +) +follower = KochFollower(config) +follower.setup_motors() +``` + + + +You should see the following instruction. +``` +Connect the controller board to the 'gripper' motor only and press enter. +``` + +As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor. + +
+Troubleshooting + + If you get an error at that point, check your cables and make sure they are plugged in properly: +
    +
  • Power supply
    • +
  • USB cable between your computer and the controller board
    • +
  • The 3-pin cable from the controller board to the motor
    • +
+ + If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB). +
+ +You should then see the following message: +``` +'gripper' motor id set to 6 +``` + +Followed by the next instruction: +``` +Connect the controller board to the 'wrist_roll' motor only and press enter. +``` + +You can disconnect the 3-pin cable from the controller board but you can leave it connected to the gripper motor on the other end as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one. + +Repeat the operation for each motor as instructed. + +> [!TIP] +> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board. + +When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm. + +#### Leader +Do the same steps for the leader arm but modify the command or script accordingly. + + + + +```bash +python -m lerobot.setup_motors \ + --teleop.type=koch_leader \ + --teleop.port=/dev/tty.usbmodem575E0031751 \ # <- paste here the port found at previous step +``` + + + +```python +from lerobot.common.teleoperators.koch_leader import KochLeader, KochLeaderConfig + +config = KochLeaderConfig( + port="/dev/tty.usbmodem575E0031751", + id="my_awesome_leader_arm", +) +leader = KochLeader(config) +leader.setup_motors() +``` + + + +## Calibrate + +Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. +The calibration process is very important because it allows a neural network trained on one robot to work on another. + +#### Follower + +Run the following command or API example to calibrate the follower arm: + + + + +```bash +python -m lerobot.calibrate \ + --robot.type=koch_follower \ + --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --robot.id=my_awesome_follower_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower + +config = KochFollowerConfig( + port="/dev/tty.usbmodem585A0076891", + id="my_awesome_follower_arm", +) + +follower = KochFollower(config) +follower.connect(calibrate=False) +follower.calibrate() +follower.disconnect() +``` + + + +We unified the calibration method for most robots. Thus, the calibration steps for this Koch arm are the same as the steps for the SO100 and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video). + +#### Leader + +Do the same steps to calibrate the leader arm, run the following command or API example: + + + + +```bash +python -m lerobot.calibrate \ + --teleop.type=koch_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader + +config = KochLeaderConfig( + port="/dev/tty.usbmodem575E0031751", + id="my_awesome_leader_arm", +) + +leader = KochLeader(config) +leader.connect(calibrate=False) +leader.calibrate() +leader.disconnect() +``` + + + +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/lerobot/common/robots/koch_follower/koch_follower.py b/lerobot/common/robots/koch_follower/koch_follower.py new file mode 100644 index 0000000000000000000000000000000000000000..8eaaecd2713925d9443bcb33a94267327c92a960 --- /dev/null +++ b/lerobot/common/robots/koch_follower/koch_follower.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property +from typing import Any + +from lerobot.common.cameras.utils import make_cameras_from_configs +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.dynamixel import ( + DynamixelMotorsBus, + OperatingMode, +) + +from ..robot import Robot +from ..utils import ensure_safe_goal_position +from .config_koch_follower import KochFollowerConfig + +logger = logging.getLogger(__name__) + + +class KochFollower(Robot): + """ + - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow + expansion, developed by Alexander Koch from [Tau Robotics](https://tau-robotics.com) + - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss + """ + + config_class = KochFollowerConfig + name = "koch_follower" + + def __init__(self, config: KochFollowerConfig): + super().__init__(config) + self.config = config + norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100 + self.bus = DynamixelMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "xl430-w250", norm_mode_body), + "shoulder_lift": Motor(2, "xl430-w250", norm_mode_body), + "elbow_flex": Motor(3, "xl330-m288", norm_mode_body), + "wrist_flex": Motor(4, "xl330-m288", norm_mode_body), + "wrist_roll": Motor(5, "xl330-m288", norm_mode_body), + "gripper": Motor(6, "xl330-m288", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + self.cameras = make_cameras_from_configs(config.cameras) + + @property + def _motors_ft(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def _cameras_ft(self) -> dict[str, tuple]: + return { + cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._motors_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._motors_ft + + @property + def is_connected(self) -> bool: + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) + + def connect(self, calibrate: bool = True) -> None: + """ + We assume that at connection time, arm is in a rest position, + and torque can be safely disabled to run calibration. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + full_turn_motors = ["shoulder_pan", "wrist_roll"] + unknown_range_motors = [motor for motor in self.bus.motors if motor not in full_turn_motors] + print( + f"Move all joints except {full_turn_motors} sequentially through their entire " + "ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + for motor in full_turn_motors: + range_mins[motor] = 0 + range_maxes[motor] = 4095 + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + logger.info(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + with self.bus.torque_disabled(): + self.bus.configure_motors() + # Use 'extended position mode' for all motors except gripper, because in joint mode the servos + # can't rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling + # the arm, you could end up with a servo with a position 0 or 4095 at a crucial point + for motor in self.bus.motors: + if motor != "gripper": + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + # Use 'position control current based' for gripper to be limited by the limit of the current. For + # the follower gripper, it means it can grasp an object without forcing too much even tho, its + # goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch). + # For the leader gripper, it means we can use it as a physical trigger, since we can force with + # our finger to make it move, and it will move back to its original target position when we + # release the force. + self.bus.write("Operating_Mode", "gripper", OperatingMode.CURRENT_POSITION.value) + + # Set better PID values to close the gap between recorded states and actions + # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor + self.bus.write("Position_P_Gain", "elbow_flex", 1500) + self.bus.write("Position_I_Gain", "elbow_flex", 0) + self.bus.write("Position_D_Gain", "elbow_flex", 600) + + def setup_motors(self) -> None: + for motor in reversed(self.bus.motors): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + def get_observation(self) -> dict[str, Any]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Read arm position + start = time.perf_counter() + obs_dict = self.bus.sync_read("Present_Position") + obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + return obs_dict + + def send_action(self, action: dict[str, float]) -> dict[str, float]: + """Command arm to move to a target joint configuration. + + The relative action magnitude may be clipped depending on the configuration parameter + `max_relative_target`. In this case, the action sent differs from original action. + Thus, this function always returns the action actually sent. + + Args: + action (dict[str, float]): The goal positions for the motors. + + Returns: + dict[str, float]: The action sent to the motors, potentially clipped. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")} + + # Cap goal position when too far away from present position. + # /!\ Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.bus.sync_read("Present_Position") + goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()} + goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target) + + # Send goal position to the arm + self.bus.sync_write("Goal_Position", goal_pos) + return {f"{motor}.pos": val for motor, val in goal_pos.items()} + + def disconnect(self): + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect(self.config.disable_torque_on_disconnect) + for cam in self.cameras.values(): + cam.disconnect() + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/robots/lekiwi/__init__.py b/lerobot/common/robots/lekiwi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..43491041e24da9995fab4d272291b9f7e208f2eb --- /dev/null +++ b/lerobot/common/robots/lekiwi/__init__.py @@ -0,0 +1,3 @@ +from .config_lekiwi import LeKiwiClientConfig, LeKiwiConfig +from .lekiwi import LeKiwi +from .lekiwi_client import LeKiwiClient diff --git a/lerobot/common/robots/lekiwi/config_lekiwi.py b/lerobot/common/robots/lekiwi/config_lekiwi.py new file mode 100644 index 0000000000000000000000000000000000000000..dd7279013455673a7aeecdc57eea1ce4fa60467d --- /dev/null +++ b/lerobot/common/robots/lekiwi/config_lekiwi.py @@ -0,0 +1,96 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.cameras.configs import CameraConfig, Cv2Rotation +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig + +from ..config import RobotConfig + + +def lekiwi_cameras_config() -> dict[str, CameraConfig]: + return { + "front": OpenCVCameraConfig( + index_or_path="/dev/video0", fps=30, width=640, height=480, rotation=Cv2Rotation.ROTATE_180 + ), + "wrist": OpenCVCameraConfig( + index_or_path="/dev/video2", fps=30, width=480, height=640, rotation=Cv2Rotation.ROTATE_90 + ), + } + + +@RobotConfig.register_subclass("lekiwi") +@dataclass +class LeKiwiConfig(RobotConfig): + port: str = "/dev/ttyACM0" # port to connect to the bus + + disable_torque_on_disconnect: bool = True + + # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. + # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as + # the number of motors in your follower arms. + max_relative_target: int | None = None + + cameras: dict[str, CameraConfig] = field(default_factory=lekiwi_cameras_config) + + # Set to `True` for backward compatibility with previous policies/dataset + use_degrees: bool = False + + +@dataclass +class LeKiwiHostConfig: + # Network Configuration + port_zmq_cmd: int = 5555 + port_zmq_observations: int = 5556 + + # Duration of the application + connection_time_s: int = 30 + + # Watchdog: stop the robot if no command is received for over 0.5 seconds. + watchdog_timeout_ms: int = 500 + + # If robot jitters decrease the frequency and monitor cpu load with `top` in cmd + max_loop_freq_hz: int = 30 + + +@RobotConfig.register_subclass("lekiwi_client") +@dataclass +class LeKiwiClientConfig(RobotConfig): + # Network Configuration + remote_ip: str + port_zmq_cmd: int = 5555 + port_zmq_observations: int = 5556 + + teleop_keys: dict[str, str] = field( + default_factory=lambda: { + # Movement + "forward": "w", + "backward": "s", + "left": "a", + "right": "d", + "rotate_left": "z", + "rotate_right": "x", + # Speed control + "speed_up": "r", + "speed_down": "f", + # quit teleop + "quit": "q", + } + ) + + cameras: dict[str, CameraConfig] = field(default_factory=lekiwi_cameras_config) + + polling_timeout_ms: int = 15 + connect_timeout_s: int = 5 diff --git a/lerobot/common/robots/lekiwi/lekiwi.mdx b/lerobot/common/robots/lekiwi/lekiwi.mdx new file mode 100644 index 0000000000000000000000000000000000000000..6645ec0a85e98625af669b57e095235daffdf4b5 --- /dev/null +++ b/lerobot/common/robots/lekiwi/lekiwi.mdx @@ -0,0 +1,300 @@ +# LeKiwi + +In the steps below, we explain how to assemble the LeKiwi mobile robot. + +## Source the parts + +Follow this [README](https://github.com/SIGRobotics-UIUC/LeKiwi). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts. +And advise if it's your first time printing or if you don't own a 3D printer. + +### Wired version +If you have the **wired** LeKiwi version, you can skip the installation of the Raspberry Pi and setting up SSH. You can also run all commands directly on your PC for both the LeKiwi scripts and the leader arm scripts for teleoperating. + +## Install software on Pi +Now we have to set up the remote PC that will run on the LeKiwi Robot. This is normally a Raspberry Pi, but can be any PC that can run on 5V and has enough usb ports (2 or more) for the cameras and motor control board. + +### Install OS +For setting up the Raspberry Pi and its SD-card see: [Setup PI](https://www.raspberrypi.com/documentation/computers/getting-started.html). Here is explained how to download the [Imager](https://www.raspberrypi.com/software/) to install Raspberry Pi OS or Ubuntu. + +### Setup SSH +After setting up your Pi, you should enable and set up [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can log in to the Pi from your laptop without requiring a screen, keyboard, and mouse on the Pi. A great tutorial on how to do this can be found [here](https://www.raspberrypi.com/documentation/computers/remote-access.html#ssh). Logging into your Pi can be done in your Command Prompt (cmd) or, if you use VSCode you can use [this](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh) extension. + +### Install LeRobot on Pi 🤗 + +On your Raspberry Pi install LeRobot using our [Installation Guide](./installation) + +In addition to these instructions, you need to install the Feetech sdk on your Pi: +```bash +pip install -e ".[feetech]" +``` + +## Install LeRobot locally +If you already have installed LeRobot on your laptop/pc you can skip this step; otherwise, please follow along as we do the same steps we did on the Pi. + +Follow our [Installation Guide](./installation) + +Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:. +Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands. + +# Step-by-Step Assembly Instructions + +First, we will assemble the two SO100/SO101 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base. The instructions for assembling can be found on these two pages: + +- [Assemble SO101](./so101#step-by-step-assembly-instructions) +- [Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi/blob/main/Assembly.md) + +### Find the USB ports associated with motor board + +To find the port for each bus servo adapter, run this script: +```bash +python lerobot/find_port.py +``` + + + + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/tty.usbmodem575E0032081'] +Remove the USB cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/tty.usbmodem575E0032081 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your board. + + + + +On Linux, you might need to give access to the USB ports by running: +```bash +sudo chmod 666 /dev/ttyACM0 +sudo chmod 666 /dev/ttyACM1 +``` + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/ttyACM0'] +Remove the usb cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/ttyACM0 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/ttyACM0` corresponding to your board. + + + + +### Configure motors +The instructions for configuring the motors can be found in the SO101 [docs](./so101#configure-the-motors). Besides the ids for the arm motors, we also need to set the motor ids for the mobile base. These need to be in a specific order to work. Below an image of the motor ids and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ids for the wheels are 7, 8 and 9. + +You can run this command to setup motors for LeKiwi. It will first setup the motors for arm (id 6..1) and then setup motors for wheels (9,8,7) + +```bash +python -m lerobot.setup_motors \ + --robot.type=lekiwi \ + --robot.port=/dev/tty.usbmodem58760431551 # <- paste here the port found at previous step +``` + +Motor ID's for mobile robot + +### Troubleshoot communication + +If you are having trouble connecting to the Mobile SO100, follow these steps to diagnose and resolve the issue. + +#### 1. Verify IP Address Configuration +Make sure that the correct IP for the Pi is used in the commands or in your code. To check the Raspberry Pi's IP address, run (on the Pi command line): +```bash +hostname -I +``` + +#### 2. Check if Pi is reachable from laptop/pc +Try pinging the Raspberry Pi from your laptop: +```bach +ping +``` + +If the ping fails: +- Ensure the Pi is powered on and connected to the same network. +- Check if SSH is enabled on the Pi. + +#### 3. Try SSH connection +If you can't SSH into the Pi, it might not be properly connected. Use: +```bash +ssh @ +``` +If you get a connection error: +- Ensure SSH is enabled on the Pi by running: + ```bash + sudo raspi-config + ``` + Then navigate to: **Interfacing Options -> SSH** and enable it. + +### Calibration + +Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated. +The calibration process is very important because it allows a neural network trained on one robot to work on another. + +### Calibrate follower arm (on mobile base) + +Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm: + +```bash +python -m lerobot.calibrate \ + --robot.type=lekiwi \ + --robot.id=my_awesome_kiwi # <- Give the robot a unique name +``` + +We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video). + +### Wired version +If you have the **wired** LeKiwi version, please run all commands on your laptop. + +### Calibrate leader arm +Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the following command of API example on your laptop: + + + +```bash +python -m lerobot.calibrate \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader + +config = SO100LeaderConfig( + port="/dev/tty.usbmodem58760431551", + id="my_awesome_leader_arm", +) + +leader = SO100Leader(config) +leader.connect(calibrate=False) +leader.calibrate() +leader.disconnect() +``` + + + +## Teleoperate LeKiwi + +> [!TIP] +> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal. + +To teleoperate, SSH into your Raspberry Pi, and run `conda activate lerobot` and this command: +```bash +python -m lerobot.common.robots.lekiwi.lekiwi_host +``` + +Then on your laptop, also run `conda activate lerobot` and run the API example, make sure you set the correct `remote_ip` and `port`. + +```bash +python examples/lekiwi/teleoperate.py +``` + +You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below: + +| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) | +| ---------- | ------------------ | ---------------------- | +| Fast | 0.4 | 90 | +| Medium | 0.25 | 60 | +| Slow | 0.1 | 30 | + + +| Key | Action | +| --- | -------------- | +| W | Move forward | +| A | Move left | +| S | Move backward | +| D | Move right | +| Z | Turn left | +| X | Turn right | +| R | Increase speed | +| F | Decrease speed | + +> [!TIP] +> If you use a different keyboard, you can change the keys for each command in the [`LeKiwiConfig`](../lerobot/common/robot_devices/robots/configs.py). + +### Wired version +If you have the **wired** LeKiwi version, please run all commands on your laptop. + +## Record a dataset + +Once you're familiar with teleoperation, you can record your first dataset. + +We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens). + +Add your token to the CLI by running this command: +```bash +huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential +``` + +Then store your Hugging Face repository name in a variable: +```bash +HF_USER=$(huggingface-cli whoami | head -n 1) +echo $HF_USER +``` + +Now you can record a dataset. To record episodes and upload your dataset to the hub, execute this API example tailored for LeKiwi. Make sure to first adapt the `remote_ip`, `repo_id`, `port` and `task` in the script. If you would like to run the script for longer you can increase `NB_CYCLES_CLIENT_CONNECTION`. +```bash +python examples/lekiwi/record.py +``` + +#### Dataset upload +Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running: +```bash +echo https://huggingface.co/datasets/${HF_USER}/so101_test +``` +Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example). + +You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot). + +#### Tips for gathering data + +Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images. + +In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions. + +Avoid adding too much variation too quickly, as it may hinder your results. + +If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset. + +#### Troubleshooting: +- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux). + + +## Replay an episode + +To replay an episode run the API example below, make sure to change `remote_ip`, `port`, LeRobotDatasetId and episode index. + + +```bash +python examples/lekiwi/replay.py +``` + +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by the training part of this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) + +## Evaluate your policy + +To evaluate your policy run the `evaluate.py` API example, make sure to change `remote_ip`, `port`, model.. + +```bash +python examples/lekiwi/evaluate.py +``` + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/lerobot/common/robots/lekiwi/lekiwi.py b/lerobot/common/robots/lekiwi/lekiwi.py new file mode 100644 index 0000000000000000000000000000000000000000..a1c5fd420d96af833ede6b964f1cd5162e2b8308 --- /dev/null +++ b/lerobot/common/robots/lekiwi/lekiwi.py @@ -0,0 +1,411 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property +from itertools import chain +from typing import Any + +import numpy as np + +from lerobot.common.cameras.utils import make_cameras_from_configs +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.feetech import ( + FeetechMotorsBus, + OperatingMode, +) + +from ..robot import Robot +from ..utils import ensure_safe_goal_position +from .config_lekiwi import LeKiwiConfig + +logger = logging.getLogger(__name__) + + +class LeKiwi(Robot): + """ + The robot includes a three omniwheel mobile base and a remote follower arm. + The leader arm is connected locally (on the laptop) and its joint positions are recorded and then + forwarded to the remote follower arm (after applying a safety clamp). + In parallel, keyboard teleoperation is used to generate raw velocity commands for the wheels. + """ + + config_class = LeKiwiConfig + name = "lekiwi" + + def __init__(self, config: LeKiwiConfig): + super().__init__(config) + self.config = config + norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100 + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + # arm + "arm_shoulder_pan": Motor(1, "sts3215", norm_mode_body), + "arm_shoulder_lift": Motor(2, "sts3215", norm_mode_body), + "arm_elbow_flex": Motor(3, "sts3215", norm_mode_body), + "arm_wrist_flex": Motor(4, "sts3215", norm_mode_body), + "arm_wrist_roll": Motor(5, "sts3215", norm_mode_body), + "arm_gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), + # base + "base_left_wheel": Motor(7, "sts3215", MotorNormMode.RANGE_M100_100), + "base_back_wheel": Motor(8, "sts3215", MotorNormMode.RANGE_M100_100), + "base_right_wheel": Motor(9, "sts3215", MotorNormMode.RANGE_M100_100), + }, + calibration=self.calibration, + ) + self.arm_motors = [motor for motor in self.bus.motors if motor.startswith("arm")] + self.base_motors = [motor for motor in self.bus.motors if motor.startswith("base")] + self.cameras = make_cameras_from_configs(config.cameras) + + @property + def _state_ft(self) -> dict[str, type]: + return dict.fromkeys( + ( + "arm_shoulder_pan.pos", + "arm_shoulder_lift.pos", + "arm_elbow_flex.pos", + "arm_wrist_flex.pos", + "arm_wrist_roll.pos", + "arm_gripper.pos", + "x.vel", + "y.vel", + "theta.vel", + ), + float, + ) + + @property + def _cameras_ft(self) -> dict[str, tuple]: + return { + cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._state_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._state_ft + + @property + def is_connected(self) -> bool: + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) + + def connect(self, calibrate: bool = True) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + + motors = self.arm_motors + self.base_motors + + self.bus.disable_torque(self.arm_motors) + for name in self.arm_motors: + self.bus.write("Operating_Mode", name, OperatingMode.POSITION.value) + + input("Move robot to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings(self.arm_motors) + + homing_offsets.update(dict.fromkeys(self.base_motors, 0)) + + full_turn_motor = [ + motor for motor in motors if any(keyword in motor for keyword in ["wheel", "wrist"]) + ] + unknown_range_motors = [motor for motor in motors if motor not in full_turn_motor] + + print( + f"Move all arm joints except '{full_turn_motor}' sequentially through their " + "entire ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + for name in full_turn_motor: + range_mins[name] = 0 + range_maxes[name] = 4095 + + self.calibration = {} + for name, motor in self.bus.motors.items(): + self.calibration[name] = MotorCalibration( + id=motor.id, + drive_mode=0, + homing_offset=homing_offsets[name], + range_min=range_mins[name], + range_max=range_maxes[name], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print("Calibration saved to", self.calibration_fpath) + + def configure(self): + # Set-up arm actuators (position mode) + # We assume that at connection time, arm is in a rest position, + # and torque can be safely disabled to run calibration. + self.bus.disable_torque() + self.bus.configure_motors() + for name in self.arm_motors: + self.bus.write("Operating_Mode", name, OperatingMode.POSITION.value) + # Set P_Coefficient to lower value to avoid shakiness (Default is 32) + self.bus.write("P_Coefficient", name, 16) + # Set I_Coefficient and D_Coefficient to default value 0 and 32 + self.bus.write("I_Coefficient", name, 0) + self.bus.write("D_Coefficient", name, 32) + + for name in self.base_motors: + self.bus.write("Operating_Mode", name, OperatingMode.VELOCITY.value) + + self.bus.enable_torque() + + def setup_motors(self) -> None: + for motor in chain(reversed(self.arm_motors), reversed(self.base_motors)): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + @staticmethod + def _degps_to_raw(degps: float) -> int: + steps_per_deg = 4096.0 / 360.0 + speed_in_steps = degps * steps_per_deg + speed_int = int(round(speed_in_steps)) + # Cap the value to fit within signed 16-bit range (-32768 to 32767) + if speed_int > 0x7FFF: + speed_int = 0x7FFF # 32767 -> maximum positive value + elif speed_int < -0x8000: + speed_int = -0x8000 # -32768 -> minimum negative value + return speed_int + + @staticmethod + def _raw_to_degps(raw_speed: int) -> float: + steps_per_deg = 4096.0 / 360.0 + magnitude = raw_speed + degps = magnitude / steps_per_deg + return degps + + def _body_to_wheel_raw( + self, + x: float, + y: float, + theta: float, + wheel_radius: float = 0.05, + base_radius: float = 0.125, + max_raw: int = 3000, + ) -> dict: + """ + Convert desired body-frame velocities into wheel raw commands. + + Parameters: + x_cmd : Linear velocity in x (m/s). + y_cmd : Linear velocity in y (m/s). + theta_cmd : Rotational velocity (deg/s). + wheel_radius: Radius of each wheel (meters). + base_radius : Distance from the center of rotation to each wheel (meters). + max_raw : Maximum allowed raw command (ticks) per wheel. + + Returns: + A dictionary with wheel raw commands: + {"base_left_wheel": value, "base_back_wheel": value, "base_right_wheel": value}. + + Notes: + - Internally, the method converts theta_cmd to rad/s for the kinematics. + - The raw command is computed from the wheels angular speed in deg/s + using _degps_to_raw(). If any command exceeds max_raw, all commands + are scaled down proportionally. + """ + # Convert rotational velocity from deg/s to rad/s. + theta_rad = theta * (np.pi / 180.0) + # Create the body velocity vector [x, y, theta_rad]. + velocity_vector = np.array([x, y, theta_rad]) + + # Define the wheel mounting angles with a -90° offset. + angles = np.radians(np.array([240, 0, 120]) - 90) + # Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed. + # The third column (base_radius) accounts for the effect of rotation. + m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles]) + + # Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s). + wheel_linear_speeds = m.dot(velocity_vector) + wheel_angular_speeds = wheel_linear_speeds / wheel_radius + + # Convert wheel angular speeds from rad/s to deg/s. + wheel_degps = wheel_angular_speeds * (180.0 / np.pi) + + # Scaling + steps_per_deg = 4096.0 / 360.0 + raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps] + max_raw_computed = max(raw_floats) + if max_raw_computed > max_raw: + scale = max_raw / max_raw_computed + wheel_degps = wheel_degps * scale + + # Convert each wheel’s angular speed (deg/s) to a raw integer. + wheel_raw = [self._degps_to_raw(deg) for deg in wheel_degps] + + return { + "base_left_wheel": wheel_raw[0], + "base_back_wheel": wheel_raw[1], + "base_right_wheel": wheel_raw[2], + } + + def _wheel_raw_to_body( + self, + left_wheel_speed, + back_wheel_speed, + right_wheel_speed, + wheel_radius: float = 0.05, + base_radius: float = 0.125, + ) -> dict[str, Any]: + """ + Convert wheel raw command feedback back into body-frame velocities. + + Parameters: + wheel_raw : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel"). + wheel_radius: Radius of each wheel (meters). + base_radius : Distance from the robot center to each wheel (meters). + + Returns: + A dict (x.vel, y.vel, theta.vel) all in m/s + """ + + # Convert each raw command back to an angular speed in deg/s. + wheel_degps = np.array( + [ + self._raw_to_degps(left_wheel_speed), + self._raw_to_degps(back_wheel_speed), + self._raw_to_degps(right_wheel_speed), + ] + ) + + # Convert from deg/s to rad/s. + wheel_radps = wheel_degps * (np.pi / 180.0) + # Compute each wheel’s linear speed (m/s) from its angular speed. + wheel_linear_speeds = wheel_radps * wheel_radius + + # Define the wheel mounting angles with a -90° offset. + angles = np.radians(np.array([240, 0, 120]) - 90) + m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles]) + + # Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds. + m_inv = np.linalg.inv(m) + velocity_vector = m_inv.dot(wheel_linear_speeds) + x, y, theta_rad = velocity_vector + theta = theta_rad * (180.0 / np.pi) + return { + "x.vel": x, + "y.vel": y, + "theta.vel": theta, + } # m/s and deg/s + + def get_observation(self) -> dict[str, Any]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Read actuators position for arm and vel for base + start = time.perf_counter() + arm_pos = self.bus.sync_read("Present_Position", self.arm_motors) + base_wheel_vel = self.bus.sync_read("Present_Velocity", self.base_motors) + + base_vel = self._wheel_raw_to_body( + base_wheel_vel["base_left_wheel"], + base_wheel_vel["base_back_wheel"], + base_wheel_vel["base_right_wheel"], + ) + + arm_state = {f"{k}.pos": v for k, v in arm_pos.items()} + + obs_dict = {**arm_state, **base_vel} + + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + return obs_dict + + def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + """Command lekiwi to move to a target joint configuration. + + The relative action magnitude may be clipped depending on the configuration parameter + `max_relative_target`. In this case, the action sent differs from original action. + Thus, this function always returns the action actually sent. + + Raises: + RobotDeviceNotConnectedError: if robot is not connected. + + Returns: + np.ndarray: the action sent to the motors, potentially clipped. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + arm_goal_pos = {k: v for k, v in action.items() if k.endswith(".pos")} + base_goal_vel = {k: v for k, v in action.items() if k.endswith(".vel")} + + base_wheel_goal_vel = self._body_to_wheel_raw( + base_goal_vel["x.vel"], base_goal_vel["y.vel"], base_goal_vel["theta.vel"] + ) + + # Cap goal position when too far away from present position. + # /!\ Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.bus.sync_read("Present_Position", self.arm_motors) + goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in arm_goal_pos.items()} + arm_safe_goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target) + arm_goal_pos = arm_safe_goal_pos + + # Send goal position to the actuators + arm_goal_pos_raw = {k.replace(".pos", ""): v for k, v in arm_goal_pos.items()} + self.bus.sync_write("Goal_Position", arm_goal_pos_raw) + self.bus.sync_write("Goal_Velocity", base_wheel_goal_vel) + + return {**arm_goal_pos, **base_goal_vel} + + def stop_base(self): + self.bus.sync_write("Goal_Velocity", dict.fromkeys(self.base_motors, 0), num_retry=5) + logger.info("Base motors stopped") + + def disconnect(self): + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.stop_base() + self.bus.disconnect(self.config.disable_torque_on_disconnect) + for cam in self.cameras.values(): + cam.disconnect() + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/robots/lekiwi/lekiwi_client.py b/lerobot/common/robots/lekiwi/lekiwi_client.py new file mode 100644 index 0000000000000000000000000000000000000000..d35a317264602f971d04e0da433b55cb0d7ce4c1 --- /dev/null +++ b/lerobot/common/robots/lekiwi/lekiwi_client.py @@ -0,0 +1,342 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# TODO(aliberts, Steven, Pepijn): use gRPC calls instead of zmq? + +import base64 +import json +import logging +from functools import cached_property +from typing import Any, Dict, Optional, Tuple + +import cv2 +import numpy as np +import torch +import zmq + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..robot import Robot +from .config_lekiwi import LeKiwiClientConfig + + +class LeKiwiClient(Robot): + config_class = LeKiwiClientConfig + name = "lekiwi_client" + + def __init__(self, config: LeKiwiClientConfig): + super().__init__(config) + self.config = config + self.id = config.id + self.robot_type = config.type + + self.remote_ip = config.remote_ip + self.port_zmq_cmd = config.port_zmq_cmd + self.port_zmq_observations = config.port_zmq_observations + + self.teleop_keys = config.teleop_keys + + self.polling_timeout_ms = config.polling_timeout_ms + self.connect_timeout_s = config.connect_timeout_s + + self.zmq_context = None + self.zmq_cmd_socket = None + self.zmq_observation_socket = None + + self.last_frames = {} + + self.last_remote_state = {} + + # Define three speed levels and a current index + self.speed_levels = [ + {"xy": 0.1, "theta": 30}, # slow + {"xy": 0.2, "theta": 60}, # medium + {"xy": 0.3, "theta": 90}, # fast + ] + self.speed_index = 0 # Start at slow + + self._is_connected = False + self.logs = {} + + @cached_property + def _state_ft(self) -> dict[str, type]: + return dict.fromkeys( + ( + "arm_shoulder_pan.pos", + "arm_shoulder_lift.pos", + "arm_elbow_flex.pos", + "arm_wrist_flex.pos", + "arm_wrist_roll.pos", + "arm_gripper.pos", + "x.vel", + "y.vel", + "theta.vel", + ), + float, + ) + + @cached_property + def _state_order(self) -> tuple[str, ...]: + return tuple(self._state_ft.keys()) + + @cached_property + def _cameras_ft(self) -> dict[str, tuple[int, int, int]]: + return {name: (cfg.height, cfg.width, 3) for name, cfg in self.config.cameras.items()} + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._state_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._state_ft + + @property + def is_connected(self) -> bool: + return self._is_connected + + @property + def is_calibrated(self) -> bool: + pass + + def connect(self) -> None: + """Establishes ZMQ sockets with the remote mobile robot""" + + if self._is_connected: + raise DeviceAlreadyConnectedError( + "LeKiwi Daemon is already connected. Do not run `robot.connect()` twice." + ) + + self.zmq_context = zmq.Context() + self.zmq_cmd_socket = self.zmq_context.socket(zmq.PUSH) + zmq_cmd_locator = f"tcp://{self.remote_ip}:{self.port_zmq_cmd}" + self.zmq_cmd_socket.connect(zmq_cmd_locator) + self.zmq_cmd_socket.setsockopt(zmq.CONFLATE, 1) + + self.zmq_observation_socket = self.zmq_context.socket(zmq.PULL) + zmq_observations_locator = f"tcp://{self.remote_ip}:{self.port_zmq_observations}" + self.zmq_observation_socket.connect(zmq_observations_locator) + self.zmq_observation_socket.setsockopt(zmq.CONFLATE, 1) + + poller = zmq.Poller() + poller.register(self.zmq_observation_socket, zmq.POLLIN) + socks = dict(poller.poll(self.connect_timeout_s * 1000)) + if self.zmq_observation_socket not in socks or socks[self.zmq_observation_socket] != zmq.POLLIN: + raise DeviceNotConnectedError("Timeout waiting for LeKiwi Host to connect expired.") + + self._is_connected = True + + def calibrate(self) -> None: + pass + + def _poll_and_get_latest_message(self) -> Optional[str]: + """Polls the ZMQ socket for a limited time and returns the latest message string.""" + poller = zmq.Poller() + poller.register(self.zmq_observation_socket, zmq.POLLIN) + + try: + socks = dict(poller.poll(self.polling_timeout_ms)) + except zmq.ZMQError as e: + logging.error(f"ZMQ polling error: {e}") + return None + + if self.zmq_observation_socket not in socks: + logging.info("No new data available within timeout.") + return None + + last_msg = None + while True: + try: + msg = self.zmq_observation_socket.recv_string(zmq.NOBLOCK) + last_msg = msg + except zmq.Again: + break + + if last_msg is None: + logging.warning("Poller indicated data, but failed to retrieve message.") + + return last_msg + + def _parse_observation_json(self, obs_string: str) -> Optional[Dict[str, Any]]: + """Parses the JSON observation string.""" + try: + return json.loads(obs_string) + except json.JSONDecodeError as e: + logging.error(f"Error decoding JSON observation: {e}") + return None + + def _decode_image_from_b64(self, image_b64: str) -> Optional[np.ndarray]: + """Decodes a base64 encoded image string to an OpenCV image.""" + if not image_b64: + return None + try: + jpg_data = base64.b64decode(image_b64) + np_arr = np.frombuffer(jpg_data, dtype=np.uint8) + frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) + if frame is None: + logging.warning("cv2.imdecode returned None for an image.") + return frame + except (TypeError, ValueError) as e: + logging.error(f"Error decoding base64 image data: {e}") + return None + + def _remote_state_from_obs( + self, observation: Dict[str, Any] + ) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]: + """Extracts frames, and state from the parsed observation.""" + flat_state = {key: value for key, value in observation.items() if key in self._state_ft} + + state_vec = np.array( + [flat_state.get(k, 0.0) for k in self._state_order], + dtype=np.float32, + ) + + # Decode images + image_observation = { + f"observation.images.{key}": value + for key, value in observation.items() + if key in self._cameras_ft + } + current_frames: Dict[str, np.ndarray] = {} + for cam_name, image_b64 in image_observation.items(): + frame = self._decode_image_from_b64(image_b64) + if frame is not None: + current_frames[cam_name] = frame + + return current_frames, {"observation.state": state_vec} + + def _get_data(self) -> Tuple[Dict[str, np.ndarray], Dict[str, Any], Dict[str, Any]]: + """ + Polls the video socket for the latest observation data. + + Attempts to retrieve and decode the latest message within a short timeout. + If successful, updates and returns the new frames, speed, and arm state. + If no new data arrives or decoding fails, returns the last known values. + """ + + # 1. Get the latest message string from the socket + latest_message_str = self._poll_and_get_latest_message() + + # 2. If no message, return cached data + if latest_message_str is None: + return self.last_frames, self.last_remote_state + + # 3. Parse the JSON message + observation = self._parse_observation_json(latest_message_str) + + # 4. If JSON parsing failed, return cached data + if observation is None: + return self.last_frames, self.last_remote_state + + # 5. Process the valid observation data + try: + new_frames, new_state = self._remote_state_from_obs(observation) + except Exception as e: + logging.error(f"Error processing observation data, serving last observation: {e}") + return self.last_frames, self.last_remote_state + + self.last_frames = new_frames + self.last_remote_state = new_state + + return new_frames, new_state + + def get_observation(self) -> dict[str, Any]: + """ + Capture observations from the remote robot: current follower arm positions, + present wheel speeds (converted to body-frame velocities: x, y, theta), + and a camera frame. Receives over ZMQ, translate to body-frame vel + """ + if not self._is_connected: + raise DeviceNotConnectedError("LeKiwiClient is not connected. You need to run `robot.connect()`.") + + frames, obs_dict = self._get_data() + + # Loop over each configured camera + for cam_name, frame in frames.items(): + if frame is None: + logging.warning("Frame is None") + frame = np.zeros((640, 480, 3), dtype=np.uint8) + obs_dict[cam_name] = torch.from_numpy(frame) + + return obs_dict + + def _from_keyboard_to_base_action(self, pressed_keys: np.ndarray): + # Speed control + if self.teleop_keys["speed_up"] in pressed_keys: + self.speed_index = min(self.speed_index + 1, 2) + if self.teleop_keys["speed_down"] in pressed_keys: + self.speed_index = max(self.speed_index - 1, 0) + speed_setting = self.speed_levels[self.speed_index] + xy_speed = speed_setting["xy"] # e.g. 0.1, 0.25, or 0.4 + theta_speed = speed_setting["theta"] # e.g. 30, 60, or 90 + + x_cmd = 0.0 # m/s forward/backward + y_cmd = 0.0 # m/s lateral + theta_cmd = 0.0 # deg/s rotation + + if self.teleop_keys["forward"] in pressed_keys: + x_cmd += xy_speed + if self.teleop_keys["backward"] in pressed_keys: + x_cmd -= xy_speed + if self.teleop_keys["left"] in pressed_keys: + y_cmd += xy_speed + if self.teleop_keys["right"] in pressed_keys: + y_cmd -= xy_speed + if self.teleop_keys["rotate_left"] in pressed_keys: + theta_cmd += theta_speed + if self.teleop_keys["rotate_right"] in pressed_keys: + theta_cmd -= theta_speed + return { + "x.vel": x_cmd, + "y.vel": y_cmd, + "theta.vel": theta_cmd, + } + + def configure(self): + pass + + def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + """Command lekiwi to move to a target joint configuration. Translates to motor space + sends over ZMQ + + Args: + action (np.ndarray): array containing the goal positions for the motors. + + Raises: + RobotDeviceNotConnectedError: if robot is not connected. + + Returns: + np.ndarray: the action sent to the motors, potentially clipped. + """ + if not self._is_connected: + raise DeviceNotConnectedError( + "ManipulatorRobot is not connected. You need to run `robot.connect()`." + ) + + self.zmq_cmd_socket.send_string(json.dumps(action)) # action is in motor space + + # TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value + actions = np.array([action.get(k, 0.0) for k in self._state_order], dtype=np.float32) + return {"action": actions} + + def disconnect(self): + """Cleans ZMQ comms""" + + if not self._is_connected: + raise DeviceNotConnectedError( + "LeKiwi is not connected. You need to run `robot.connect()` before disconnecting." + ) + self.zmq_observation_socket.close() + self.zmq_cmd_socket.close() + self.zmq_context.term() + self._is_connected = False diff --git a/lerobot/common/robots/lekiwi/lekiwi_host.py b/lerobot/common/robots/lekiwi/lekiwi_host.py new file mode 100644 index 0000000000000000000000000000000000000000..064b3141c146ed04f5bd1b86ab121f3d68581c26 --- /dev/null +++ b/lerobot/common/robots/lekiwi/lekiwi_host.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import base64 +import json +import logging +import time + +import cv2 +import zmq + +from .config_lekiwi import LeKiwiConfig, LeKiwiHostConfig +from .lekiwi import LeKiwi + + +class LeKiwiHost: + def __init__(self, config: LeKiwiHostConfig): + self.zmq_context = zmq.Context() + self.zmq_cmd_socket = self.zmq_context.socket(zmq.PULL) + self.zmq_cmd_socket.setsockopt(zmq.CONFLATE, 1) + self.zmq_cmd_socket.bind(f"tcp://*:{config.port_zmq_cmd}") + + self.zmq_observation_socket = self.zmq_context.socket(zmq.PUSH) + self.zmq_observation_socket.setsockopt(zmq.CONFLATE, 1) + self.zmq_observation_socket.bind(f"tcp://*:{config.port_zmq_observations}") + + self.connection_time_s = config.connection_time_s + self.watchdog_timeout_ms = config.watchdog_timeout_ms + self.max_loop_freq_hz = config.max_loop_freq_hz + + def disconnect(self): + self.zmq_observation_socket.close() + self.zmq_cmd_socket.close() + self.zmq_context.term() + + +def main(): + logging.info("Configuring LeKiwi") + robot_config = LeKiwiConfig() + robot = LeKiwi(robot_config) + + logging.info("Connecting LeKiwi") + robot.connect() + + logging.info("Starting HostAgent") + host_config = LeKiwiHostConfig() + host = LeKiwiHost(host_config) + + last_cmd_time = time.time() + watchdog_active = False + logging.info("Waiting for commands...") + try: + # Business logic + start = time.perf_counter() + duration = 0 + while duration < host.connection_time_s: + loop_start_time = time.time() + try: + msg = host.zmq_cmd_socket.recv_string(zmq.NOBLOCK) + data = dict(json.loads(msg)) + _action_sent = robot.send_action(data) + last_cmd_time = time.time() + watchdog_active = False + except zmq.Again: + if not watchdog_active: + logging.warning("No command available") + except Exception as e: + logging.error("Message fetching failed: %s", e) + + now = time.time() + if (now - last_cmd_time > host.watchdog_timeout_ms / 1000) and not watchdog_active: + logging.warning( + f"Command not received for more than {host.watchdog_timeout_ms} milliseconds. Stopping the base." + ) + watchdog_active = True + robot.stop_base() + + last_observation = robot.get_observation() + + # Encode ndarrays to base64 strings + for cam_key, _ in robot.cameras.items(): + ret, buffer = cv2.imencode( + ".jpg", last_observation[cam_key], [int(cv2.IMWRITE_JPEG_QUALITY), 90] + ) + if ret: + last_observation[cam_key] = base64.b64encode(buffer).decode("utf-8") + else: + last_observation[cam_key] = "" + + # Send the observation to the remote agent + try: + host.zmq_observation_socket.send_string(json.dumps(last_observation), flags=zmq.NOBLOCK) + except zmq.Again: + logging.info("Dropping observation, no client connected") + + # Ensure a short sleep to avoid overloading the CPU. + elapsed = time.time() - loop_start_time + + time.sleep(max(1 / host.max_loop_freq_hz - elapsed, 0)) + duration = time.perf_counter() - start + print("Cycle time reached.") + + except KeyboardInterrupt: + print("Keyboard interrupt received. Exiting...") + finally: + print("Shutting down Lekiwi Host.") + robot.disconnect() + host.disconnect() + + logging.info("Finished LeKiwi cleanly") + + +if __name__ == "__main__": + main() diff --git a/lerobot/common/robots/robot.py b/lerobot/common/robots/robot.py new file mode 100644 index 0000000000000000000000000000000000000000..ba9a5b0e3a2b3efce0dd01051b3ec2ce3add0b21 --- /dev/null +++ b/lerobot/common/robots/robot.py @@ -0,0 +1,184 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from pathlib import Path +from typing import Any + +import draccus + +from lerobot.common.constants import HF_LEROBOT_CALIBRATION, ROBOTS +from lerobot.common.motors import MotorCalibration + +from .config import RobotConfig + + +# TODO(aliberts): action/obs typing such as Generic[ObsType, ActType] similar to gym.Env ? +# https://github.com/Farama-Foundation/Gymnasium/blob/3287c869f9a48d99454306b0d4b4ec537f0f35e3/gymnasium/core.py#L23 +class Robot(abc.ABC): + """ + The base abstract class for all LeRobot-compatible robots. + + This class provides a standardized interface for interacting with physical robots. + Subclasses must implement all abstract methods and properties to be usable. + + Attributes: + config_class (RobotConfig): The expected configuration class for this robot. + name (str): The unique robot name used to identify this robot type. + """ + + # Set these in ALL subclasses + config_class: RobotConfig + name: str + + def __init__(self, config: RobotConfig): + self.robot_type = self.name + self.id = config.id + self.calibration_dir = ( + config.calibration_dir if config.calibration_dir else HF_LEROBOT_CALIBRATION / ROBOTS / self.name + ) + self.calibration_dir.mkdir(parents=True, exist_ok=True) + self.calibration_fpath = self.calibration_dir / f"{self.id}.json" + self.calibration: dict[str, MotorCalibration] = {} + if self.calibration_fpath.is_file(): + self._load_calibration() + + def __str__(self) -> str: + return f"{self.id} {self.__class__.__name__}" + + # TODO(aliberts): create a proper Feature class for this that links with datasets + @property + @abc.abstractmethod + def observation_features(self) -> dict: + """ + A dictionary describing the structure and types of the observations produced by the robot. + Its structure (keys) should match the structure of what is returned by :pymeth:`get_observation`. + Values for the dict should either be: + - The type of the value if it's a simple value, e.g. `float` for single proprioceptive value (a joint's position/velocity) + - A tuple representing the shape if it's an array-type value, e.g. `(height, width, channel)` for images + + Note: this property should be able to be called regardless of whether the robot is connected or not. + """ + pass + + @property + @abc.abstractmethod + def action_features(self) -> dict: + """ + A dictionary describing the structure and types of the actions expected by the robot. Its structure + (keys) should match the structure of what is passed to :pymeth:`send_action`. Values for the dict + should be the type of the value if it's a simple value, e.g. `float` for single proprioceptive value + (a joint's goal position/velocity) + + Note: this property should be able to be called regardless of whether the robot is connected or not. + """ + pass + + @property + @abc.abstractmethod + def is_connected(self) -> bool: + """ + Whether the robot is currently connected or not. If `False`, calling :pymeth:`get_observation` or + :pymeth:`send_action` should raise an error. + """ + pass + + @abc.abstractmethod + def connect(self, calibrate: bool = True) -> None: + """ + Establish communication with the robot. + + Args: + calibrate (bool): If True, automatically calibrate the robot after connecting if it's not + calibrated or needs calibration (this is hardware-dependant). + """ + pass + + @property + @abc.abstractmethod + def is_calibrated(self) -> bool: + """Whether the robot is currently calibrated or not. Should be always `True` if not applicable""" + pass + + @abc.abstractmethod + def calibrate(self) -> None: + """ + Calibrate the robot if applicable. If not, this should be a no-op. + + This method should collect any necessary data (e.g., motor offsets) and update the + :pyattr:`calibration` dictionary accordingly. + """ + pass + + def _load_calibration(self, fpath: Path | None = None) -> None: + """ + Helper to load calibration data from the specified file. + + Args: + fpath (Path | None): Optional path to the calibration file. Defaults to `self.calibration_fpath`. + """ + fpath = self.calibration_fpath if fpath is None else fpath + with open(fpath) as f, draccus.config_type("json"): + self.calibration = draccus.load(dict[str, MotorCalibration], f) + + def _save_calibration(self, fpath: Path | None = None) -> None: + """ + Helper to save calibration data to the specified file. + + Args: + fpath (Path | None): Optional path to save the calibration file. Defaults to `self.calibration_fpath`. + """ + fpath = self.calibration_fpath if fpath is None else fpath + with open(fpath, "w") as f, draccus.config_type("json"): + draccus.dump(self.calibration, f, indent=4) + + @abc.abstractmethod + def configure(self) -> None: + """ + Apply any one-time or runtime configuration to the robot. + This may include setting motor parameters, control modes, or initial state. + """ + pass + + @abc.abstractmethod + def get_observation(self) -> dict[str, Any]: + """ + Retrieve the current observation from the robot. + + Returns: + dict[str, Any]: A flat dictionary representing the robot's current sensory state. Its structure + should match :pymeth:`observation_features`. + """ + + pass + + @abc.abstractmethod + def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + """ + Send an action command to the robot. + + Args: + action (dict[str, Any]): Dictionary representing the desired action. Its structure should match + :pymeth:`action_features`. + + Returns: + dict[str, Any]: The action actually sent to the motors potentially clipped or modified, e.g. by + safety limits on velocity. + """ + pass + + @abc.abstractmethod + def disconnect(self) -> None: + """Disconnect from the robot and perform any necessary cleanup.""" + pass diff --git a/lerobot/common/robots/so100_follower/__init__.py b/lerobot/common/robots/so100_follower/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..42abdfc5f66becbdd8c250baae67e5c9780a005d --- /dev/null +++ b/lerobot/common/robots/so100_follower/__init__.py @@ -0,0 +1,3 @@ +from .config_so100_follower import SO100FollowerConfig, SO100FollowerEndEffectorConfig +from .so100_follower import SO100Follower +from .so100_follower_end_effector import SO100FollowerEndEffector diff --git a/lerobot/common/robots/so100_follower/config_so100_follower.py b/lerobot/common/robots/so100_follower/config_so100_follower.py new file mode 100644 index 0000000000000000000000000000000000000000..7ab1ac955a18e368fa4f03c4e6ec4d53db3d1736 --- /dev/null +++ b/lerobot/common/robots/so100_follower/config_so100_follower.py @@ -0,0 +1,63 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.cameras import CameraConfig + +from ..config import RobotConfig + + +@RobotConfig.register_subclass("so100_follower") +@dataclass +class SO100FollowerConfig(RobotConfig): + # Port to connect to the arm + port: str + + disable_torque_on_disconnect: bool = True + + # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. + # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as + # the number of motors in your follower arms. + max_relative_target: int | None = None + + # cameras + cameras: dict[str, CameraConfig] = field(default_factory=dict) + + # Set to `True` for backward compatibility with previous policies/dataset + use_degrees: bool = False + + +@RobotConfig.register_subclass("so100_follower_end_effector") +@dataclass +class SO100FollowerEndEffectorConfig(SO100FollowerConfig): + """Configuration for the SO100FollowerEndEffector robot.""" + + # Default bounds for the end-effector position (in meters) + end_effector_bounds: dict[str, list[float]] = field( + default_factory=lambda: { + "min": [-1.0, -1.0, -1.0], # min x, y, z + "max": [1.0, 1.0, 1.0], # max x, y, z + } + ) + + max_gripper_pos: float = 50 + + end_effector_step_sizes: dict[str, float] = field( + default_factory=lambda: { + "x": 0.02, + "y": 0.02, + "z": 0.02, + } + ) diff --git a/lerobot/common/robots/so100_follower/so100.mdx b/lerobot/common/robots/so100_follower/so100.mdx new file mode 100644 index 0000000000000000000000000000000000000000..af814d3edc1ba9e553abea0b615b75d632b71945 --- /dev/null +++ b/lerobot/common/robots/so100_follower/so100.mdx @@ -0,0 +1,486 @@ +# SO-100 + +In the steps below, we explain how to assemble the SO-100 robot. + +## Source the parts + +Follow this [README](https://github.com/TheRobotStudio/SO-ARM100/blob/main/SO100.md). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts. And advise if it's your first time printing or if you don't own a 3D printer. + +## Install LeRobot 🤗 + +To install LeRobot, follow our [Installation Guide](./installation) + +In addition to these instructions, you need to install the Feetech SDK: +```bash +pip install -e ".[feetech]" +``` + +## Step-by-Step Assembly Instructions + +## Remove the gears of the 6 leader motors + +
+Video removing gears + +
+ +
+ +
+ +Follow the video for removing gears. You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm. + +### Clean Parts +Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material. + +### Additional Guidance + +
+Video assembling arms + +
+ +
+ +
+ +**Note:** +This video provides visual guidance for assembling the arms, but it doesn't specify when or how to do the wiring. Inserting the cables beforehand is much easier than doing it afterward. The first arm may take a bit more than 1 hour to assemble, but once you get used to it, you can assemble the second arm in under 1 hour. + +--- + +### First Motor + +**Step 2: Insert Wires** +- Insert two wires into the first motor. + + + +**Step 3: Install in Base** +- Place the first motor into the base. + + + +**Step 4: Secure Motor** +- Fasten the motor with 4 screws. Two from the bottom and two from top. + +**Step 5: Attach Motor Holder** +- Slide over the first motor holder and fasten it using two screws (one on each side). + + + +**Step 6: Attach Motor Horns** +- Install both motor horns, securing the top horn with a screw. Try not to move the motor position when attaching the motor horn, especially for the leader arms, where we removed the gears. + + + +
+ Video adding motor horn + +
+ +**Step 7: Attach Shoulder Part** +- Route one wire to the back of the robot and the other to the left or towards you (see photo). +- Attach the shoulder part. + + + +**Step 8: Secure Shoulder** +- Tighten the shoulder part with 4 screws on top and 4 on the bottom +*(access bottom holes by turning the shoulder).* + +--- + +### Second Motor Assembly + +**Step 9: Install Motor 2** +- Slide the second motor in from the top and link the wire from motor 1 to motor 2. + + + +**Step 10: Attach Shoulder Holder** +- Add the shoulder motor holder. +- Ensure the wire from motor 1 to motor 2 goes behind the holder while the other wire is routed upward (see photo). +- This part can be tight to assemble, you can use a workbench like the image or a similar setup to push the part around the motor. + +
+ + + +
+ +**Step 11: Secure Motor 2** +- Fasten the second motor with 4 screws. + +**Step 12: Attach Motor Horn** +- Attach both motor horns to motor 2, again use the horn screw. + +**Step 13: Attach Base** +- Install the base attachment using 2 screws. + + + +**Step 14: Attach Upper Arm** +- Attach the upper arm with 4 screws on each side. + + + +--- + +### Third Motor Assembly + +**Step 15: Install Motor 3** +- Route the motor cable from motor 2 through the cable holder to motor 3, then secure motor 3 with 4 screws. + +**Step 16: Attach Motor Horn** +- Attach both motor horns to motor 3 and secure one again with a horn screw. + + + +**Step 17: Attach Forearm** +- Connect the forearm to motor 3 using 4 screws on each side. + + + +--- + +### Fourth Motor Assembly + +**Step 18: Install Motor 4** +- Slide in motor 4, attach the cable from motor 3, and secure the cable in its holder with a screw. + +
+ + +
+ +**Step 19: Attach Motor Holder 4** +- Install the fourth motor holder (a tight fit). Ensure one wire is routed upward and the wire from motor 3 is routed downward (see photo). + + + +**Step 20: Secure Motor 4 & Attach Horn** +- Fasten motor 4 with 4 screws and attach its motor horns, use for one a horn screw. + + + +--- + +### Wrist Assembly + +**Step 21: Install Motor 5** +- Insert motor 5 into the wrist holder and secure it with 2 front screws. + + + +**Step 22: Attach Wrist** +- Connect the wire from motor 4 to motor 5. And already insert the other wire for the gripper. +- Secure the wrist to motor 4 using 4 screws on both sides. + + + +**Step 23: Attach Wrist Horn** +- Install only one motor horn on the wrist motor and secure it with a horn screw. + + + +--- + +### Follower Configuration + +**Step 24: Attach Gripper** +- Attach the gripper to motor 5. + + + +**Step 25: Install Gripper Motor** +- Insert the gripper motor, connect the motor wire from motor 5 to motor 6, and secure it with 3 screws on each side. + + + +**Step 26: Attach Gripper Horn & Claw** +- Attach the motor horns and again use a horn screw. +- Install the gripper claw and secure it with 4 screws on both sides. + + + +**Step 27: Mount Controller** +- Attach the motor controller to the back of the robot. + +
+ + +
+ +*Assembly complete – proceed to Leader arm assembly.* + +--- + +### Leader Configuration + +For the leader configuration, perform **Steps 1–23**. Make sure that you removed the motor gears from the motors. + +**Step 24: Attach Leader Holder** +- Mount the leader holder onto the wrist and secure it with a screw. + + + +**Step 25: Attach Handle** +- Attach the handle to motor 5 using 4 screws. + + + +**Step 26: Install Gripper Motor** +- Insert the gripper motor, secure it with 3 screws on each side, attach a motor horn using a horn screw, and connect the motor wire. + + + +**Step 27: Attach Trigger** +- Attach the follower trigger with 4 screws. + + + +**Step 28: Mount Controller** +- Attach the motor controller to the back of the robot. + +
+ + +
+ +## Configure the motors + +### 1. Find the USB ports associated with each arm + +To find the port for each bus servo adapter, run this script: +```bash +python lerobot/find_port.py +``` + + + + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751'] +Remove the USB cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/tty.usbmodem575E0032081 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm. + + + + +On Linux, you might need to give access to the USB ports by running: +```bash +sudo chmod 666 /dev/ttyACM0 +sudo chmod 666 /dev/ttyACM1 +``` + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/ttyACM0', '/dev/ttyACM1'] +Remove the usb cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/ttyACM1 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm. + + + + +### 2. Set the motors ids and baudrates + +Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate. + +To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once. + +If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match. + +#### Follower + +Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter. + +For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm. + + + + +```bash +python -m lerobot.setup_motors \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem585A0076841 # <- paste here the port found at previous step +``` + + + +```python +from lerobot.common.robots.so100_follower import SO100Follower, SO100FollowerConfig + +config = SO100FollowerConfig( + port="/dev/tty.usbmodem585A0076841", + id="my_awesome_follower_arm", +) +follower = SO100Follower(config) +follower.setup_motors() +``` + + + +You should see the following instruction +``` +Connect the controller board to the 'gripper' motor only and press enter. +``` + +As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor. + +
+Troubleshooting + + If you get an error at that point, check your cables and make sure they are plugged in properly: +
    +
  • Power supply
    • +
  • USB cable between your computer and the controller board
    • +
  • The 3-pin cable from the controller board to the motor
    • +
+ +If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB). +
+ +You should then see the following message: +``` +'gripper' motor id set to 6 +``` + +Followed by the next instruction: +``` +Connect the controller board to the 'wrist_roll' motor only and press enter. +``` + +You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one. + +Repeat the operation for each motor as instructed. + +> [!TIP] +> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board. + +When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm. + +#### Leader +Do the same steps for the leader arm. + + + +```bash +python -m lerobot.setup_motors \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step +``` + + + +```python +from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig + +config = SO100LeaderConfig( + port="/dev/tty.usbmodem585A0076841", + id="my_awesome_leader_arm", +) +leader = SO100Leader(config) +leader.setup_motors() +``` + + + +## Calibrate + +Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. +The calibration process is very important because it allows a neural network trained on one robot to work on another. + +#### Follower + +Run the following command or API example to calibrate the follower arm: + + + + +```bash +python -m lerobot.calibrate \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --robot.id=my_awesome_follower_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.robots.so100_follower import SO100FollowerConfig, SO100Follower + +config = SO100FollowerConfig( + port="/dev/tty.usbmodem585A0076891", + id="my_awesome_follower_arm", +) + +follower = SO100Follower(config) +follower.connect(calibrate=False) +follower.calibrate() +follower.disconnect() +``` + + + +We unified the calibration method for most robots. Thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video) + +#### Leader + +Do the same steps to calibrate the leader arm, run the following command or API example: + + + + +```bash +python -m lerobot.calibrate \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader + +config = SO100LeaderConfig( + port="/dev/tty.usbmodem58760431551", + id="my_awesome_leader_arm", +) + +leader = SO100Leader(config) +leader.connect(calibrate=False) +leader.calibrate() +leader.disconnect() +``` + + + +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/lerobot/common/robots/so100_follower/so100_follower.py b/lerobot/common/robots/so100_follower/so100_follower.py new file mode 100644 index 0000000000000000000000000000000000000000..34867b81922649fa129530a47c09e7e7140d0986 --- /dev/null +++ b/lerobot/common/robots/so100_follower/so100_follower.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property +from typing import Any + +from lerobot.common.cameras.utils import make_cameras_from_configs +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.feetech import ( + FeetechMotorsBus, + OperatingMode, +) + +from ..robot import Robot +from ..utils import ensure_safe_goal_position +from .config_so100_follower import SO100FollowerConfig + +logger = logging.getLogger(__name__) + + +class SO100Follower(Robot): + """ + [SO-100 Follower Arm](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio + """ + + config_class = SO100FollowerConfig + name = "so100_follower" + + def __init__(self, config: SO100FollowerConfig): + super().__init__(config) + self.config = config + norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100 + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "sts3215", norm_mode_body), + "shoulder_lift": Motor(2, "sts3215", norm_mode_body), + "elbow_flex": Motor(3, "sts3215", norm_mode_body), + "wrist_flex": Motor(4, "sts3215", norm_mode_body), + "wrist_roll": Motor(5, "sts3215", norm_mode_body), + "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + self.cameras = make_cameras_from_configs(config.cameras) + + @property + def _motors_ft(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def _cameras_ft(self) -> dict[str, tuple]: + return { + cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._motors_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._motors_ft + + @property + def is_connected(self) -> bool: + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) + + def connect(self, calibrate: bool = True) -> None: + """ + We assume that at connection time, arm is in a rest position, + and torque can be safely disabled to run calibration. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + full_turn_motor = "wrist_roll" + unknown_range_motors = [motor for motor in self.bus.motors if motor != full_turn_motor] + print( + f"Move all joints except '{full_turn_motor}' sequentially through their " + "entire ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + range_mins[full_turn_motor] = 0 + range_maxes[full_turn_motor] = 4095 + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print("Calibration saved to", self.calibration_fpath) + + def configure(self) -> None: + with self.bus.torque_disabled(): + self.bus.configure_motors() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + # Set P_Coefficient to lower value to avoid shakiness (Default is 32) + self.bus.write("P_Coefficient", motor, 16) + # Set I_Coefficient and D_Coefficient to default value 0 and 32 + self.bus.write("I_Coefficient", motor, 0) + self.bus.write("D_Coefficient", motor, 32) + + def setup_motors(self) -> None: + for motor in reversed(self.bus.motors): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + def get_observation(self) -> dict[str, Any]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Read arm position + start = time.perf_counter() + obs_dict = self.bus.sync_read("Present_Position") + obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + return obs_dict + + def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + """Command arm to move to a target joint configuration. + + The relative action magnitude may be clipped depending on the configuration parameter + `max_relative_target`. In this case, the action sent differs from original action. + Thus, this function always returns the action actually sent. + + Raises: + RobotDeviceNotConnectedError: if robot is not connected. + + Returns: + the action sent to the motors, potentially clipped. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")} + + # Cap goal position when too far away from present position. + # /!\ Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.bus.sync_read("Present_Position") + goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()} + goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target) + + # Send goal position to the arm + self.bus.sync_write("Goal_Position", goal_pos) + return {f"{motor}.pos": val for motor, val in goal_pos.items()} + + def disconnect(self): + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect(self.config.disable_torque_on_disconnect) + for cam in self.cameras.values(): + cam.disconnect() + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/robots/so100_follower/so100_follower_end_effector.py b/lerobot/common/robots/so100_follower/so100_follower_end_effector.py new file mode 100644 index 0000000000000000000000000000000000000000..04ab34a07be4e3cc293260d6d6ff8b496f250c9c --- /dev/null +++ b/lerobot/common/robots/so100_follower/so100_follower_end_effector.py @@ -0,0 +1,193 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from typing import Any + +import numpy as np + +from lerobot.common.cameras import make_cameras_from_configs +from lerobot.common.errors import DeviceNotConnectedError +from lerobot.common.model.kinematics import RobotKinematics +from lerobot.common.motors import Motor, MotorNormMode +from lerobot.common.motors.feetech import FeetechMotorsBus + +from . import SO100Follower +from .config_so100_follower import SO100FollowerEndEffectorConfig + +logger = logging.getLogger(__name__) +EE_FRAME = "gripper_tip" + + +class SO100FollowerEndEffector(SO100Follower): + """ + SO100Follower robot with end-effector space control. + + This robot inherits from SO100Follower but transforms actions from + end-effector space to joint space before sending them to the motors. + """ + + config_class = SO100FollowerEndEffectorConfig + name = "so100_follower_end_effector" + + def __init__(self, config: SO100FollowerEndEffectorConfig): + super().__init__(config) + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "sts3215", MotorNormMode.DEGREES), + "shoulder_lift": Motor(2, "sts3215", MotorNormMode.DEGREES), + "elbow_flex": Motor(3, "sts3215", MotorNormMode.DEGREES), + "wrist_flex": Motor(4, "sts3215", MotorNormMode.DEGREES), + "wrist_roll": Motor(5, "sts3215", MotorNormMode.DEGREES), + "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + + self.cameras = make_cameras_from_configs(config.cameras) + + self.config = config + + # Initialize the kinematics module for the so100 robot + self.kinematics = RobotKinematics(robot_type="so_new_calibration") + + # Store the bounds for end-effector position + self.end_effector_bounds = self.config.end_effector_bounds + + self.current_ee_pos = None + self.current_joint_pos = None + + @property + def action_features(self) -> dict[str, Any]: + """ + Define action features for end-effector control. + Returns dictionary with dtype, shape, and names. + """ + return { + "dtype": "float32", + "shape": (4,), + "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2, "gripper": 3}, + } + + def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + """ + Transform action from end-effector space to joint space and send to motors. + + Args: + action: Dictionary with keys 'delta_x', 'delta_y', 'delta_z' for end-effector control + or a numpy array with [delta_x, delta_y, delta_z] + + Returns: + The joint-space action that was sent to the motors + """ + + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Convert action to numpy array if not already + if isinstance(action, dict): + if all(k in action for k in ["delta_x", "delta_y", "delta_z"]): + delta_ee = np.array( + [ + action["delta_x"] * self.config.end_effector_step_sizes["x"], + action["delta_y"] * self.config.end_effector_step_sizes["y"], + action["delta_z"] * self.config.end_effector_step_sizes["z"], + ], + dtype=np.float32, + ) + if "gripper" not in action: + action["gripper"] = [1.0] + action = np.append(delta_ee, action["gripper"]) + else: + logger.warning( + f"Expected action keys 'delta_x', 'delta_y', 'delta_z', got {list(action.keys())}" + ) + action = np.zeros(4, dtype=np.float32) + + if self.current_joint_pos is None: + # Read current joint positions + current_joint_pos = self.bus.sync_read("Present_Position") + self.current_joint_pos = np.array([current_joint_pos[name] for name in self.bus.motors]) + + # Calculate current end-effector position using forward kinematics + if self.current_ee_pos is None: + self.current_ee_pos = self.kinematics.forward_kinematics(self.current_joint_pos, frame=EE_FRAME) + + # Set desired end-effector position by adding delta + desired_ee_pos = np.eye(4) + desired_ee_pos[:3, :3] = self.current_ee_pos[:3, :3] # Keep orientation + + # Add delta to position and clip to bounds + desired_ee_pos[:3, 3] = self.current_ee_pos[:3, 3] + action[:3] + if self.end_effector_bounds is not None: + desired_ee_pos[:3, 3] = np.clip( + desired_ee_pos[:3, 3], + self.end_effector_bounds["min"], + self.end_effector_bounds["max"], + ) + + # Compute inverse kinematics to get joint positions + target_joint_values_in_degrees = self.kinematics.ik( + self.current_joint_pos, desired_ee_pos, position_only=True, frame=EE_FRAME + ) + + target_joint_values_in_degrees = np.clip(target_joint_values_in_degrees, -180.0, 180.0) + # Create joint space action dictionary + joint_action = { + f"{key}.pos": target_joint_values_in_degrees[i] for i, key in enumerate(self.bus.motors.keys()) + } + + # Handle gripper separately if included in action + # Gripper delta action is in the range 0 - 2, + # We need to shift the action to the range -1, 1 so that we can expand it to -Max_gripper_pos, Max_gripper_pos + joint_action["gripper.pos"] = np.clip( + self.current_joint_pos[-1] + (action[-1] - 1) * self.config.max_gripper_pos, + 5, + self.config.max_gripper_pos, + ) + + self.current_ee_pos = desired_ee_pos.copy() + self.current_joint_pos = target_joint_values_in_degrees.copy() + self.current_joint_pos[-1] = joint_action["gripper.pos"] + + # Send joint space action to parent class + return super().send_action(joint_action) + + def get_observation(self) -> dict[str, Any]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Read arm position + start = time.perf_counter() + obs_dict = self.bus.sync_read("Present_Position") + obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + return obs_dict + + def reset(self): + self.current_ee_pos = None + self.current_joint_pos = None diff --git a/lerobot/common/robots/so101_follower/__init__.py b/lerobot/common/robots/so101_follower/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d0261d357fbae0035bed8704f1b9312810b3ec --- /dev/null +++ b/lerobot/common/robots/so101_follower/__init__.py @@ -0,0 +1,2 @@ +from .config_so101_follower import SO101FollowerConfig +from .so101_follower import SO101Follower diff --git a/lerobot/common/robots/so101_follower/config_so101_follower.py b/lerobot/common/robots/so101_follower/config_so101_follower.py new file mode 100644 index 0000000000000000000000000000000000000000..6178d0b18f4a58a112f1221d57d2593cb56a812c --- /dev/null +++ b/lerobot/common/robots/so101_follower/config_so101_follower.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.cameras import CameraConfig + +from ..config import RobotConfig + + +@RobotConfig.register_subclass("so101_follower") +@dataclass +class SO101FollowerConfig(RobotConfig): + # Port to connect to the arm + port: str + + disable_torque_on_disconnect: bool = True + + # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. + # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as + # the number of motors in your follower arms. + max_relative_target: int | None = None + + # cameras + cameras: dict[str, CameraConfig] = field(default_factory=dict) + + # Set to `True` for backward compatibility with previous policies/dataset + use_degrees: bool = False diff --git a/lerobot/common/robots/so101_follower/so101.mdx b/lerobot/common/robots/so101_follower/so101.mdx new file mode 100644 index 0000000000000000000000000000000000000000..636d3501162b50d05968c89221014c2401687689 --- /dev/null +++ b/lerobot/common/robots/so101_follower/so101.mdx @@ -0,0 +1,381 @@ +# SO-101 + +In the steps below, we explain how to assemble our flagship robot, the SO-101. + +## Source the parts + +Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts. +And advise if it's your first time printing or if you don't own a 3D printer. + +## Install LeRobot 🤗 + +To install LeRobot, follow our [Installation Guide](./installation) + +In addition to these instructions, you need to install the Feetech SDK: +```bash +pip install -e ".[feetech]" +``` + +## Step-by-Step Assembly Instructions + +The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader, however, uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in the table below. + +| Leader-Arm Axis | Motor | Gear Ratio | +|-----------------|:-------:|:----------:| +| Base / Shoulder Yaw | 1 | 1 / 191 | +| Shoulder Pitch | 2 | 1 / 345 | +| Elbow | 3 | 1 / 191 | +| Wrist Roll | 4 | 1 / 147 | +| Wrist Pitch | 5 | 1 / 147 | +| Gripper | 6 | 1 / 147 | + +### Clean Parts +Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material. + +### Joint 1 + +- Place the first motor into the base. +- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom. +- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side). +- Install both motor horns, securing the top horn with a M3x6mm screw. +- Attach the shoulder part. +- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom +- Add the shoulder motor holder. + +
+ +
+ +### Joint 2 + +- Slide the second motor in from the top. +- Fasten the second motor with 4 M2x6mm screws. +- Attach both motor horns to motor 2, again use the M3x6mm horn screw. +- Attach the upper arm with 4 M3x6mm screws on each side. + +
+ +
+ +### Joint 3 + +- Insert motor 3 and fasten using 4 M2x6mm screws +- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw. +- Connect the forearm to motor 3 using 4 M3x6mm screws on each side. + +
+ +
+ +### Joint 4 + +- Slide over motor holder 4. +- Slide in motor 4. +- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw. + +
+ +
+ +### Joint 5 + +- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws. +- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw. +- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides. + +
+ +
+ +### Gripper / Handle + + + + +- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws. +- Insert the gripper motor and secure it with 2 M2x6mm screws on each side. +- Attach the motor horns and again use a M3x6mm horn screw. +- Install the gripper claw and secure it with 4 M3x6mm screws on both sides. + +
+ +
+ + + + +- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws. +- Attach the handle to motor 5 using 1 M2x6mm screw. +- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw. +- Attach the follower trigger with 4 M3x6mm screws. + +
+ +
+ + + + +## Configure the motors + +### 1. Find the USB ports associated with each arm + +To find the port for each bus servo adapter, run this script: +```bash +python lerobot/find_port.py +``` + + + + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751'] +Remove the USB cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/tty.usbmodem575E0032081 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm. + + + + +On Linux, you might need to give access to the USB ports by running: +```bash +sudo chmod 666 /dev/ttyACM0 +sudo chmod 666 /dev/ttyACM1 +``` + +Example output: + +``` +Finding all available ports for the MotorBus. +['/dev/ttyACM0', '/dev/ttyACM1'] +Remove the usb cable from your MotorsBus and press Enter when done. + +[...Disconnect corresponding leader or follower arm and press Enter...] + +The port of this MotorsBus is /dev/ttyACM1 +Reconnect the USB cable. +``` + +Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm. + + + + +### 2. Set the motors ids and baudrates + +Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate. + +To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once. + +If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match. + +The video below shows the sequence of steps for setting the motor ids. + +##### Setup motors video + +
+ +
+ +#### Follower + +Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter. + + + + +```bash +python -m lerobot.setup_motors \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem585A0076841 # <- paste here the port found at previous step +``` + + + +```python +from lerobot.common.robots.so101_follower import SO101Follower, SO101FollowerConfig + +config = SO101FollowerConfig( + port="/dev/tty.usbmodem585A0076841", + id="my_awesome_follower_arm", +) +follower = SO101Follower(config) +follower.setup_motors() +``` + + + +You should see the following instruction +```bash +Connect the controller board to the 'gripper' motor only and press enter. +``` + +As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor. + +
+Troubleshooting + + If you get an error at that point, check your cables and make sure they are plugged in properly: +
    +
  • Power supply
    • +
  • USB cable between your computer and the controller board
    • +
  • The 3-pin cable from the controller board to the motor
    • +
+ + If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB). +
+ +You should then see the following message: +```bash +'gripper' motor id set to 6 +``` + +Followed by the next instruction: +```bash +Connect the controller board to the 'wrist_roll' motor only and press enter. +``` + +You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one. + +Repeat the operation for each motor as instructed. + +> [!TIP] +> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board. + +When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm. + +#### Leader +Do the same steps for the leader arm. + + + + +```bash +python -m lerobot.setup_motors \ + --teleop.type=so101_leader \ + --teleop.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step +``` + + + +```python +from lerobot.common.teleoperators.so101_leader import SO101Leader, SO101LeaderConfig + +config = SO101LeaderConfig( + port="/dev/tty.usbmodem585A0076841", + id="my_awesome_leader_arm", +) +leader = SO101Leader(config) +leader.setup_motors() +``` + + + +## Calibrate + +Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. +The calibration process is very important because it allows a neural network trained on one robot to work on another. + +#### Follower + +Run the following command or API example to calibrate the follower arm: + + + + +```bash +python -m lerobot.calibrate \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --robot.id=my_awesome_follower_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower + +config = SO101FollowerConfig( + port="/dev/tty.usbmodem585A0076891", + id="my_awesome_follower_arm", +) + +follower = SO101Follower(config) +follower.connect(calibrate=False) +follower.calibrate() +follower.disconnect() +``` + + + +The video below shows how to perform the calibration. First you need to move the robot to the position where all joints are in the middle of their ranges. Then after pressing enter you have to move each joint through its full range of motion. + +##### Calibration video + +
+ +
+ +#### Leader + +Do the same steps to calibrate the leader arm, run the following command or API example: + + + + +```bash +python -m lerobot.calibrate \ + --teleop.type=so101_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot + --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name +``` + + + +```python +from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader + +config = SO101LeaderConfig( + port="/dev/tty.usbmodem58760431551", + id="my_awesome_leader_arm", +) + +leader = SO101Leader(config) +leader.connect(calibrate=False) +leader.calibrate() +leader.disconnect() +``` + + + +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) + +> [!TIP] +> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/lerobot/common/robots/so101_follower/so101_follower.py b/lerobot/common/robots/so101_follower/so101_follower.py new file mode 100644 index 0000000000000000000000000000000000000000..41c9d4fbcc264a52297961c78e2e13cc216f0808 --- /dev/null +++ b/lerobot/common/robots/so101_follower/so101_follower.py @@ -0,0 +1,210 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property +from typing import Any + +from lerobot.common.cameras.utils import make_cameras_from_configs +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.feetech import ( + FeetechMotorsBus, + OperatingMode, +) + +from ..robot import Robot +from ..utils import ensure_safe_goal_position +from .config_so101_follower import SO101FollowerConfig + +logger = logging.getLogger(__name__) + + +class SO101Follower(Robot): + """ + SO-101 Follower Arm designed by TheRobotStudio and Hugging Face. + """ + + config_class = SO101FollowerConfig + name = "so101_follower" + + def __init__(self, config: SO101FollowerConfig): + super().__init__(config) + self.config = config + norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100 + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "sts3215", norm_mode_body), + "shoulder_lift": Motor(2, "sts3215", norm_mode_body), + "elbow_flex": Motor(3, "sts3215", norm_mode_body), + "wrist_flex": Motor(4, "sts3215", norm_mode_body), + "wrist_roll": Motor(5, "sts3215", norm_mode_body), + "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + self.cameras = make_cameras_from_configs(config.cameras) + + @property + def _motors_ft(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def _cameras_ft(self) -> dict[str, tuple]: + return { + cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._motors_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._motors_ft + + @property + def is_connected(self) -> bool: + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) + + def connect(self, calibrate: bool = True) -> None: + """ + We assume that at connection time, arm is in a rest position, + and torque can be safely disabled to run calibration. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + print( + "Move all joints sequentially through their entire ranges " + "of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion() + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print("Calibration saved to", self.calibration_fpath) + + def configure(self) -> None: + with self.bus.torque_disabled(): + self.bus.configure_motors() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + # Set P_Coefficient to lower value to avoid shakiness (Default is 32) + self.bus.write("P_Coefficient", motor, 16) + # Set I_Coefficient and D_Coefficient to default value 0 and 32 + self.bus.write("I_Coefficient", motor, 0) + self.bus.write("D_Coefficient", motor, 32) + + def setup_motors(self) -> None: + for motor in reversed(self.bus.motors): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + def get_observation(self) -> dict[str, Any]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Read arm position + start = time.perf_counter() + obs_dict = self.bus.sync_read("Present_Position") + obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + return obs_dict + + def send_action(self, action: dict[str, Any]) -> dict[str, Any]: + """Command arm to move to a target joint configuration. + + The relative action magnitude may be clipped depending on the configuration parameter + `max_relative_target`. In this case, the action sent differs from original action. + Thus, this function always returns the action actually sent. + + Raises: + RobotDeviceNotConnectedError: if robot is not connected. + + Returns: + the action sent to the motors, potentially clipped. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")} + + # Cap goal position when too far away from present position. + # /!\ Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.bus.sync_read("Present_Position") + goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()} + goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target) + + # Send goal position to the arm + self.bus.sync_write("Goal_Position", goal_pos) + return {f"{motor}.pos": val for motor, val in goal_pos.items()} + + def disconnect(self): + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect(self.config.disable_torque_on_disconnect) + for cam in self.cameras.values(): + cam.disconnect() + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/robots/stretch3/README.md b/lerobot/common/robots/stretch3/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7b8b7d7dd40ed1d4347d379cdb9cca610374c36e --- /dev/null +++ b/lerobot/common/robots/stretch3/README.md @@ -0,0 +1,161 @@ +This tutorial explains how to use [Stretch 3](https://hello-robot.com/stretch-3-product) with LeRobot. + +## Setup + +Familiarize yourself with Stretch by following its [tutorials](https://docs.hello-robot.com/0.3/getting_started/hello_robot/) (recommended). + +To use LeRobot on Stretch, 3 options are available: +- [tethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup) +- [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup) +- ssh directly into Stretch (you will first need to install and configure openssh-server on stretch using one of the two above setups) + + +## Install LeRobot + +On Stretch's CLI, follow these steps: + +1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install): +```bash +mkdir -p ~/miniconda3 +wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh +bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 +rm ~/miniconda3/miniconda.sh +~/miniconda3/bin/conda init bash +``` + +2. Comment out these lines in `~/.profile` (this can mess up paths used by conda and ~/.local/bin should already be in your PATH) +``` +# set PATH so it includes user's private bin if it exists +if [ -d "$HOME/.local/bin" ] ; then + PATH="$HOME/.local/bin:$PATH" +fi +``` + +3. Restart shell or `source ~/.bashrc` + +4. Create and activate a fresh conda environment for lerobot +```bash +conda create -y -n lerobot python=3.10 && conda activate lerobot +``` + +5. Clone LeRobot: +```bash +git clone https://github.com/huggingface/lerobot.git ~/lerobot +``` + +6. When using `miniconda`, install `ffmpeg` in your environment: +```bash +conda install ffmpeg -c conda-forge +``` + +7. Install LeRobot with stretch dependencies: +```bash +cd ~/lerobot && pip install -e ".[stretch]" +``` + +> **Note:** If you get this message, you can ignore it: `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.` + +8. Run a [system check](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#system-check) to make sure your robot is ready: +```bash +stretch_system_check.py +``` + +> **Note:** You may need to free the "robot process" after booting Stretch by running `stretch_free_robot_process.py`. For more info this Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#turning-off-gamepad-teleoperation). + +You should get something like this: +```bash +For use with S T R E T C H (R) from Hello Robot Inc. +--------------------------------------------------------------------- + +Model = Stretch 3 +Tool = DexWrist 3 w/ Gripper +Serial Number = stretch-se3-3054 + +---- Checking Hardware ---- +[Pass] Comms are ready +[Pass] Actuators are ready +[Warn] Sensors not ready (IMU AZ = -10.19 out of range -10.1 to -9.5) +[Pass] Battery voltage is 13.6 V + +---- Checking Software ---- +[Pass] Ubuntu 22.04 is ready +[Pass] All APT pkgs are setup correctly +[Pass] Firmware is up-to-date +[Pass] Python pkgs are up-to-date +[Pass] ROS2 Humble is ready +``` + +## Teleoperate, record a dataset and run a policy + +**Calibrate (Optional)** +Before operating Stretch, you need to [home](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#homing) it first. Be mindful about giving Stretch some space as this procedure will move the robot's arm and gripper. Now run this command: +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=stretch \ + --control.type=calibrate +``` +This is equivalent to running `stretch_robot_home.py` + +> **Note:** If you run any of the LeRobot scripts below and Stretch is not properly homed, it will automatically home/calibrate first. + +**Teleoperate** +Before trying teleoperation, you need to activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation). + +Now try out teleoperation (see above documentation to learn about the gamepad controls): + +> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`. +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=stretch \ + --control.type=teleoperate +``` +This is essentially the same as running `stretch_gamepad_teleop.py` + +**Record a dataset** +Once you're familiar with the gamepad controls and after a bit of practice, you can try to record your first dataset with Stretch. + +If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens): +```bash +huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential +``` + +Store your Hugging Face repository name in a variable to run these commands: +```bash +HF_USER=$(huggingface-cli whoami | head -n 1) +echo $HF_USER +``` + +Record one episode: +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=stretch \ + --control.type=record \ + --control.fps=30 \ + --control.single_task="Grasp a lego block and put it in the bin." \ + --control.repo_id=${HF_USER}/stretch_test \ + --control.tags='["tutorial"]' \ + --control.warmup_time_s=5 \ + --control.episode_time_s=30 \ + --control.reset_time_s=30 \ + --control.num_episodes=2 \ + --control.push_to_hub=true +``` + +> **Note:** If you're using ssh to connect to Stretch and run this script, you won't be able to visualize its cameras feed (though they will still be recording). To see the cameras stream, use [tethered](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup) or [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup). + +**Replay an episode** +Now try to replay this episode (make sure the robot's initial position is the same): +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=stretch \ + --control.type=replay \ + --control.fps=30 \ + --control.repo_id=${HF_USER}/stretch_test \ + --control.episode=0 +``` + +Follow [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) to train a policy on your data and run inference on your robot. You will need to adapt the code for Stretch. + +> TODO(rcadene, aliberts): Add already setup environment and policy yaml configuration files + +If you need help, please reach out on Discord in the channel `#stretch3-mobile-arm`. diff --git a/lerobot/common/robots/stretch3/__init__.py b/lerobot/common/robots/stretch3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f11d489ef817fba1d26c3f2f0a860274a703d46 --- /dev/null +++ b/lerobot/common/robots/stretch3/__init__.py @@ -0,0 +1,2 @@ +from .configuration_stretch3 import Stretch3RobotConfig +from .robot_stretch3 import Stretch3Robot diff --git a/lerobot/common/robots/stretch3/configuration_stretch3.py b/lerobot/common/robots/stretch3/configuration_stretch3.py new file mode 100644 index 0000000000000000000000000000000000000000..20ec6d02dd5498288ec14f0940e2924089466a99 --- /dev/null +++ b/lerobot/common/robots/stretch3/configuration_stretch3.py @@ -0,0 +1,58 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.cameras import CameraConfig +from lerobot.common.cameras.opencv import OpenCVCameraConfig +from lerobot.common.cameras.realsense import RealSenseCameraConfig + +from ..config import RobotConfig + + +@RobotConfig.register_subclass("stretch3") +@dataclass +class Stretch3RobotConfig(RobotConfig): + # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. + # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as + # the number of motors in your follower arms. + max_relative_target: int | None = None + + # cameras + cameras: dict[str, CameraConfig] = field( + default_factory=lambda: { + "navigation": OpenCVCameraConfig( + index_or_path="/dev/hello-nav-head-camera", + fps=10, + width=1280, + height=720, + rotation=-90, + ), + "head": RealSenseCameraConfig( + name="Intel RealSense D435I", + fps=30, + width=640, + height=480, + rotation=90, + ), + "wrist": RealSenseCameraConfig( + name="Intel RealSense D405", + fps=30, + width=640, + height=480, + ), + } + ) + + mock: bool = False diff --git a/lerobot/common/robots/stretch3/robot_stretch3.py b/lerobot/common/robots/stretch3/robot_stretch3.py new file mode 100644 index 0000000000000000000000000000000000000000..17cefeaccc4963f3c88b7f8fd74acc96c18c9de7 --- /dev/null +++ b/lerobot/common/robots/stretch3/robot_stretch3.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time + +import numpy as np +from stretch_body.gamepad_teleop import GamePadTeleop +from stretch_body.robot import Robot as StretchAPI +from stretch_body.robot_params import RobotParams + +from lerobot.common.cameras.utils import make_cameras_from_configs +from lerobot.common.constants import OBS_IMAGES, OBS_STATE +from lerobot.common.datasets.utils import get_nested_item + +from ..robot import Robot +from .configuration_stretch3 import Stretch3RobotConfig + +# {lerobot_keys: stretch.api.keys} +STRETCH_MOTORS = { + "head_pan.pos": "head.head_pan.pos", + "head_tilt.pos": "head.head_tilt.pos", + "lift.pos": "lift.pos", + "arm.pos": "arm.pos", + "wrist_pitch.pos": "end_of_arm.wrist_pitch.pos", + "wrist_roll.pos": "end_of_arm.wrist_roll.pos", + "wrist_yaw.pos": "end_of_arm.wrist_yaw.pos", + "gripper.pos": "end_of_arm.stretch_gripper.pos", + "base_x.vel": "base.x_vel", + "base_y.vel": "base.y_vel", + "base_theta.vel": "base.theta_vel", +} + + +class Stretch3Robot(Robot): + """[Stretch 3](https://hello-robot.com/stretch-3-product), by Hello Robot.""" + + config_class = Stretch3RobotConfig + name = "stretch3" + + def __init__(self, config: Stretch3RobotConfig): + raise NotImplementedError + super().__init__(config) + + self.config = config + self.robot_type = self.config.type + + self.api = StretchAPI() + self.cameras = make_cameras_from_configs(config.cameras) + + self.is_connected = False + self.logs = {} + + self.teleop = None # TODO remove + + # TODO(aliberts): test this + RobotParams.set_logging_level("WARNING") + RobotParams.set_logging_formatter("brief_console_formatter") + + self.state_keys = None + self.action_keys = None + + @property + def observation_features(self) -> dict: + return { + "dtype": "float32", + "shape": (len(STRETCH_MOTORS),), + "names": {"motors": list(STRETCH_MOTORS)}, + } + + @property + def action_features(self) -> dict: + return self.observation_features + + @property + def camera_features(self) -> dict[str, dict]: + cam_ft = {} + for cam_key, cam in self.cameras.items(): + cam_ft[cam_key] = { + "shape": (cam.height, cam.width, cam.channels), + "names": ["height", "width", "channels"], + "info": None, + } + return cam_ft + + def connect(self) -> None: + self.is_connected = self.api.startup() + if not self.is_connected: + print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'") + raise ConnectionError() + + for cam in self.cameras.values(): + cam.connect() + self.is_connected = self.is_connected and cam.is_connected + + if not self.is_connected: + print("Could not connect to the cameras, check that all cameras are plugged-in.") + raise ConnectionError() + + self.calibrate() + + def calibrate(self) -> None: + if not self.api.is_homed(): + self.api.home() + + def _get_state(self) -> dict: + status = self.api.get_status() + return {k: get_nested_item(status, v, sep=".") for k, v in STRETCH_MOTORS.items()} + + def get_observation(self) -> dict[str, np.ndarray]: + obs_dict = {} + + # Read Stretch state + before_read_t = time.perf_counter() + state = self._get_state() + self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t + + if self.state_keys is None: + self.state_keys = list(state) + + state = np.asarray(list(state.values())) + obs_dict[OBS_STATE] = state + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + before_camread_t = time.perf_counter() + obs_dict[f"{OBS_IMAGES}.{cam_key}"] = cam.async_read() + self.logs[f"read_camera_{cam_key}_dt_s"] = cam.logs["delta_timestamp_s"] + self.logs[f"async_read_camera_{cam_key}_dt_s"] = time.perf_counter() - before_camread_t + + return obs_dict + + def send_action(self, action: np.ndarray) -> np.ndarray: + if not self.is_connected: + raise ConnectionError() + + if self.teleop is None: + self.teleop = GamePadTeleop(robot_instance=False) + self.teleop.startup(robot=self) + + if self.action_keys is None: + dummy_action = self.teleop.gamepad_controller.get_state() + self.action_keys = list(dummy_action.keys()) + + action_dict = dict(zip(self.action_keys, action.tolist(), strict=True)) + + before_write_t = time.perf_counter() + self.teleop.do_motion(state=action_dict, robot=self) + self.push_command() + self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t + + # TODO(aliberts): return action_sent when motion is limited + return action + + def print_logs(self) -> None: + pass + # TODO(aliberts): move robot-specific logs logic here + + def teleop_safety_stop(self) -> None: + if self.teleop is not None: + self.teleop._safety_stop(robot=self) + + def disconnect(self) -> None: + self.api.stop() + if self.teleop is not None: + self.teleop.gamepad_controller.stop() + self.teleop.stop() + + for cam in self.cameras.values(): + cam.disconnect() + + self.is_connected = False diff --git a/lerobot/common/robots/utils.py b/lerobot/common/robots/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e2664727a6a3e29b4557ac73a71829d671be7f31 --- /dev/null +++ b/lerobot/common/robots/utils.py @@ -0,0 +1,95 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from pprint import pformat + +from lerobot.common.robots import RobotConfig + +from .robot import Robot + + +def make_robot_from_config(config: RobotConfig) -> Robot: + if config.type == "koch_follower": + from .koch_follower import KochFollower + + return KochFollower(config) + elif config.type == "so100_follower": + from .so100_follower import SO100Follower + + return SO100Follower(config) + elif config.type == "so100_follower_end_effector": + from .so100_follower import SO100FollowerEndEffector + + return SO100FollowerEndEffector(config) + elif config.type == "so101_follower": + from .so101_follower import SO101Follower + + return SO101Follower(config) + elif config.type == "lekiwi": + from .lekiwi import LeKiwi + + return LeKiwi(config) + elif config.type == "stretch3": + from .stretch3 import Stretch3Robot + + return Stretch3Robot(config) + elif config.type == "viperx": + from .viperx import ViperX + + return ViperX(config) + elif config.type == "mock_robot": + from tests.mocks.mock_robot import MockRobot + + return MockRobot(config) + else: + raise ValueError(config.type) + + +def ensure_safe_goal_position( + goal_present_pos: dict[str, tuple[float, float]], max_relative_target: float | dict[float] +) -> dict[str, float]: + """Caps relative action target magnitude for safety.""" + + if isinstance(max_relative_target, float): + diff_cap = dict.fromkeys(goal_present_pos, max_relative_target) + elif isinstance(max_relative_target, dict): + if not set(goal_present_pos) == set(max_relative_target): + raise ValueError("max_relative_target keys must match those of goal_present_pos.") + diff_cap = max_relative_target + else: + raise TypeError(max_relative_target) + + warnings_dict = {} + safe_goal_positions = {} + for key, (goal_pos, present_pos) in goal_present_pos.items(): + diff = goal_pos - present_pos + max_diff = diff_cap[key] + safe_diff = min(diff, max_diff) + safe_diff = max(safe_diff, -max_diff) + safe_goal_pos = present_pos + safe_diff + safe_goal_positions[key] = safe_goal_pos + if abs(safe_goal_pos - goal_pos) > 1e-4: + warnings_dict[key] = { + "original goal_pos": goal_pos, + "safe goal_pos": safe_goal_pos, + } + + if warnings_dict: + logging.warning( + "Relative goal position magnitude had to be clamped to be safe.\n" + f"{pformat(warnings_dict, indent=4)}" + ) + + return safe_goal_positions diff --git a/lerobot/common/robots/viperx/README.md b/lerobot/common/robots/viperx/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c29d0c0521761cb88abf50ac5df2bd911f9a3e12 --- /dev/null +++ b/lerobot/common/robots/viperx/README.md @@ -0,0 +1,182 @@ +This tutorial explains how to use [Aloha and Aloha 2 stationary](https://www.trossenrobotics.com/aloha-stationary) with LeRobot. + +## Setup + +Follow the [documentation from Trossen Robotics](https://docs.trossenrobotics.com/aloha_docs/2.0/getting_started/stationary/hardware_setup.html) for setting up the hardware and plugging the 4 arms and 4 cameras to your computer. + + +## Install LeRobot + +On your computer: + +1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install): +```bash +mkdir -p ~/miniconda3 +wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh +bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 +rm ~/miniconda3/miniconda.sh +~/miniconda3/bin/conda init bash +``` + +2. Restart shell or `source ~/.bashrc` + +3. Create and activate a fresh conda environment for lerobot +```bash +conda create -y -n lerobot python=3.10 && conda activate lerobot +``` + +4. Clone LeRobot: +```bash +git clone https://github.com/huggingface/lerobot.git ~/lerobot +``` + +5. When using `miniconda`, install `ffmpeg` in your environment: +```bash +conda install ffmpeg -c conda-forge +``` + +6. Install LeRobot with dependencies for the Aloha motors (dynamixel) and cameras (intelrealsense): +```bash +cd ~/lerobot && pip install -e ".[dynamixel, intelrealsense]" +``` + +## Teleoperate + +**/!\ FOR SAFETY, READ THIS /!\** +Teleoperation consists in manually operating the leader arms to move the follower arms. Importantly: +1. Make sure your leader arms are in the same position as the follower arms, so that the follower arms don't move too fast to match the leader arms, +2. Our code assumes that your robot has been assembled following Trossen Robotics instructions. This allows us to skip calibration, as we use the pre-defined calibration files in `.cache/calibration/aloha_default`. If you replace a motor, make sure you follow the exact instructions from Trossen Robotics. + +By running the following code, you can start your first **SAFE** teleoperation: + +> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`. + +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=aloha \ + --robot.max_relative_target=5 \ + --control.type=teleoperate +``` + +By adding `--robot.max_relative_target=5`, we override the default value for `max_relative_target` defined in [`AlohaRobotConfig`](lerobot/common/robot_devices/robots/configs.py). It is expected to be `5` to limit the magnitude of the movement for more safety, but the teleoperation won't be smooth. When you feel confident, you can disable this limit by adding `--robot.max_relative_target=null` to the command line: +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=aloha \ + --robot.max_relative_target=null \ + --control.type=teleoperate +``` + +## Record a dataset + +Once you're familiar with teleoperation, you can record your first dataset with Aloha. + +If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens): +```bash +huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential +``` + +Store your Hugging Face repository name in a variable to run these commands: +```bash +HF_USER=$(huggingface-cli whoami | head -n 1) +echo $HF_USER +``` + +Record 2 episodes and upload your dataset to the hub: +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=aloha \ + --robot.max_relative_target=null \ + --control.type=record \ + --control.fps=30 \ + --control.single_task="Grasp a lego block and put it in the bin." \ + --control.repo_id=${HF_USER}/aloha_test \ + --control.tags='["tutorial"]' \ + --control.warmup_time_s=5 \ + --control.episode_time_s=30 \ + --control.reset_time_s=30 \ + --control.num_episodes=2 \ + --control.push_to_hub=true +``` + +## Visualize a dataset + +If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by: +```bash +echo ${HF_USER}/aloha_test +``` + +If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with: +```bash +python lerobot/scripts/visualize_dataset_html.py \ + --repo-id ${HF_USER}/aloha_test +``` + +## Replay an episode + +**/!\ FOR SAFETY, READ THIS /!\** +Replay consists in automatically replaying the sequence of actions (i.e. goal positions for your motors) recorded in a given dataset episode. Make sure the current initial position of your robot is similar to the one in your episode, so that your follower arms don't move too fast to go to the first goal positions. For safety, you might want to add `--robot.max_relative_target=5` to your command line as explained above. + +Now try to replay the first episode on your robot: +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=aloha \ + --robot.max_relative_target=null \ + --control.type=replay \ + --control.fps=30 \ + --control.repo_id=${HF_USER}/aloha_test \ + --control.episode=0 +``` + +## Train a policy + +To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command: +```bash +python lerobot/scripts/train.py \ + --dataset.repo_id=${HF_USER}/aloha_test \ + --policy.type=act \ + --output_dir=outputs/train/act_aloha_test \ + --job_name=act_aloha_test \ + --policy.device=cuda \ + --wandb.enable=true +``` + +Let's explain it: +1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/aloha_test`. +2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset. +4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon. +5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`. + +For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md) + +Training should take several hours. You will find checkpoints in `outputs/train/act_aloha_test/checkpoints`. + +## Evaluate your policy + +You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes: +```bash +python lerobot/scripts/control_robot.py \ + --robot.type=aloha \ + --control.type=record \ + --control.fps=30 \ + --control.single_task="Grasp a lego block and put it in the bin." \ + --control.repo_id=${HF_USER}/eval_act_aloha_test \ + --control.tags='["tutorial"]' \ + --control.warmup_time_s=5 \ + --control.episode_time_s=30 \ + --control.reset_time_s=30 \ + --control.num_episodes=10 \ + --control.push_to_hub=true \ + --control.policy.path=outputs/train/act_aloha_test/checkpoints/last/pretrained_model \ + --control.num_image_writer_processes=1 +``` + +As you can see, it's almost the same command as previously used to record your training dataset. Two things changed: +1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_aloha_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_aloha_test`). +2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_aloha_test`). +3. We use `--control.num_image_writer_processes=1` instead of the default value (`0`). On our computer, using a dedicated process to write images from the 4 cameras on disk allows to reach constant 30 fps during inference. Feel free to explore different values for `--control.num_image_writer_processes`. + +## More + +Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth explanation. + +If you have any question or need help, please reach out on Discord in the channel `#aloha-arm`. diff --git a/lerobot/common/robots/viperx/__init__.py b/lerobot/common/robots/viperx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e24cbe3e9d71448ebd0691439ca035b0279ab103 --- /dev/null +++ b/lerobot/common/robots/viperx/__init__.py @@ -0,0 +1,2 @@ +from .config_viperx import ViperXConfig +from .viperx import ViperX diff --git a/lerobot/common/robots/viperx/config_viperx.py b/lerobot/common/robots/viperx/config_viperx.py new file mode 100644 index 0000000000000000000000000000000000000000..c82f36971e27dcc00fff6fce673cc1a4413d8bbd --- /dev/null +++ b/lerobot/common/robots/viperx/config_viperx.py @@ -0,0 +1,45 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common.cameras import CameraConfig + +from ..config import RobotConfig + + +@RobotConfig.register_subclass("viperx") +@dataclass +class ViperXConfig(RobotConfig): + port: str # Port to connect to the arm + + disable_torque_on_disconnect: bool = True + + # /!\ FOR SAFETY, READ THIS /!\ + # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. + # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as + # the number of motors in your follower arms. + # For Aloha, for every goal position request, motor rotations are capped at 5 degrees by default. + # When you feel more confident with teleoperation or running the policy, you can extend + # this safety limit and even removing it by setting it to `null`. + # Also, everything is expected to work safely out-of-the-box, but we highly advise to + # first try to teleoperate the grippers only (by commenting out the rest of the motors in this yaml), + # then to gradually add more motors (by uncommenting), until you can teleoperate both arms fully + max_relative_target: int | None = 5 + + # cameras + cameras: dict[str, CameraConfig] = field(default_factory=dict) + # Troubleshooting: If one of your IntelRealSense cameras freeze during + # data recording due to bandwidth limit, you might need to plug the camera + # on another USB hub or PCIe card. diff --git a/lerobot/common/robots/viperx/viperx.py b/lerobot/common/robots/viperx/viperx.py new file mode 100644 index 0000000000000000000000000000000000000000..3613ea6a039edd6c4e9d5ba5fdfa3b1e402e4c22 --- /dev/null +++ b/lerobot/common/robots/viperx/viperx.py @@ -0,0 +1,233 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property +from typing import Any + +from lerobot.common.cameras.utils import make_cameras_from_configs +from lerobot.common.constants import OBS_STATE +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.dynamixel import ( + DynamixelMotorsBus, + OperatingMode, +) + +from ..robot import Robot +from ..utils import ensure_safe_goal_position +from .config_viperx import ViperXConfig + +logger = logging.getLogger(__name__) + + +class ViperX(Robot): + """ + [ViperX](https://www.trossenrobotics.com/viperx-300) developed by Trossen Robotics + """ + + config_class = ViperXConfig + name = "viperx" + + def __init__( + self, + config: ViperXConfig, + ): + raise NotImplementedError + super().__init__(config) + self.config = config + self.bus = DynamixelMotorsBus( + port=self.config.port, + motors={ + "waist": Motor(1, "xm540-w270", MotorNormMode.RANGE_M100_100), + "shoulder": Motor(2, "xm540-w270", MotorNormMode.RANGE_M100_100), + "shoulder_shadow": Motor(3, "xm540-w270", MotorNormMode.RANGE_M100_100), + "elbow": Motor(4, "xm540-w270", MotorNormMode.RANGE_M100_100), + "elbow_shadow": Motor(5, "xm540-w270", MotorNormMode.RANGE_M100_100), + "forearm_roll": Motor(6, "xm540-w270", MotorNormMode.RANGE_M100_100), + "wrist_angle": Motor(7, "xm540-w270", MotorNormMode.RANGE_M100_100), + "wrist_rotate": Motor(8, "xm430-w350", MotorNormMode.RANGE_M100_100), + "gripper": Motor(9, "xm430-w350", MotorNormMode.RANGE_0_100), + }, + ) + self.cameras = make_cameras_from_configs(config.cameras) + + @property + def _motors_ft(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def _cameras_ft(self) -> dict[str, tuple]: + return { + cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._motors_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._motors_ft + + @property + def is_connected(self) -> bool: + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) + + def connect(self, calibrate: bool = True) -> None: + """ + We assume that at connection time, arm is in a rest position, + and torque can be safely disabled to run calibration. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + raise NotImplementedError # TODO(aliberts): adapt code below (copied from koch + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + input("Move robot to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + full_turn_motors = ["shoulder_pan", "wrist_roll"] + unknown_range_motors = [motor for motor in self.bus.motors if motor not in full_turn_motors] + print( + f"Move all joints except {full_turn_motors} sequentially through their entire " + "ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + for motor in full_turn_motors: + range_mins[motor] = 0 + range_maxes[motor] = 4095 + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + logger.info(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + with self.bus.torque_disabled(): + self.bus.configure_motors() + + # Set secondary/shadow ID for shoulder and elbow. These joints have two motors. + # As a result, if only one of them is required to move to a certain position, + # the other will follow. This is to avoid breaking the motors. + self.bus.write("Secondary_ID", "shoulder_shadow", 2) + self.bus.write("Secondary_ID", "elbow_shadow", 4) + + # Set a velocity limit of 131 as advised by Trossen Robotics + # TODO(aliberts): remove as it's actually useless in position control + self.bus.write("Velocity_Limit", 131) + + # Use 'extended position mode' for all motors except gripper, because in joint mode the servos + # can't rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling + # the arm, you could end up with a servo with a position 0 or 4095 at a crucial point. + # See: https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11 + for motor in self.bus.motors: + if motor != "gripper": + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + # Use 'position control current based' for follower gripper to be limited by the limit of the + # current. It can grasp an object without forcing too much even tho, it's goal position is a + # complete grasp (both gripper fingers are ordered to join and reach a touch). + self.bus.write("Operating_Mode", "gripper", OperatingMode.CURRENT_POSITION.value) + + def get_observation(self) -> dict[str, Any]: + """The returned observations do not have a batch dimension.""" + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + obs_dict = {} + + # Read arm position + start = time.perf_counter() + obs_dict[OBS_STATE] = self.bus.sync_read("Present_Position") + obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + return obs_dict + + def send_action(self, action: dict[str, float]) -> dict[str, float]: + """Command arm to move to a target joint configuration. + + The relative action magnitude may be clipped depending on the configuration parameter + `max_relative_target`. In this case, the action sent differs from original action. + Thus, this function always returns the action actually sent. + + Args: + action (dict[str, float]): The goal positions for the motors. + + Returns: + dict[str, float]: The action sent to the motors, potentially clipped. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")} + + # Cap goal position when too far away from present position. + # /!\ Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.bus.sync_read("Present_Position") + goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()} + goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target) + + # Send goal position to the arm + self.bus.sync_write("Goal_Position", goal_pos) + return {f"{motor}.pos": val for motor, val in goal_pos.items()} + + def disconnect(self): + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect(self.config.disable_torque_on_disconnect) + for cam in self.cameras.values(): + cam.disconnect() + + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/teleoperators/__init__.py b/lerobot/common/teleoperators/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..340e03badf4f7374eff444c37ddf9240df9288f5 --- /dev/null +++ b/lerobot/common/teleoperators/__init__.py @@ -0,0 +1,3 @@ +from .config import TeleoperatorConfig +from .teleoperator import Teleoperator +from .utils import make_teleoperator_from_config diff --git a/lerobot/common/teleoperators/config.py b/lerobot/common/teleoperators/config.py new file mode 100644 index 0000000000000000000000000000000000000000..91431670ce3bad5b07ec868f76c2eb3d10943c28 --- /dev/null +++ b/lerobot/common/teleoperators/config.py @@ -0,0 +1,31 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from dataclasses import dataclass +from pathlib import Path + +import draccus + + +@dataclass(kw_only=True) +class TeleoperatorConfig(draccus.ChoiceRegistry, abc.ABC): + # Allows to distinguish between different teleoperators of the same type + id: str | None = None + # Directory to store calibration file + calibration_dir: Path | None = None + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) diff --git a/lerobot/common/teleoperators/gamepad/__init__.py b/lerobot/common/teleoperators/gamepad/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1cd2ef3a167f70fac6edb6a178958480bf67d2f4 --- /dev/null +++ b/lerobot/common/teleoperators/gamepad/__init__.py @@ -0,0 +1,18 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .configuration_gamepad import GamepadTeleopConfig +from .teleop_gamepad import GamepadTeleop diff --git a/lerobot/common/teleoperators/gamepad/configuration_gamepad.py b/lerobot/common/teleoperators/gamepad/configuration_gamepad.py new file mode 100644 index 0000000000000000000000000000000000000000..c89b45c9755969343722fb98ad8b0113e8e73686 --- /dev/null +++ b/lerobot/common/teleoperators/gamepad/configuration_gamepad.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("gamepad") +@dataclass +class GamepadTeleopConfig(TeleoperatorConfig): + use_gripper: bool = True diff --git a/lerobot/common/teleoperators/gamepad/gamepad_utils.py b/lerobot/common/teleoperators/gamepad/gamepad_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5b89a5dcdacd827e80e8de945746cb078e228693 --- /dev/null +++ b/lerobot/common/teleoperators/gamepad/gamepad_utils.py @@ -0,0 +1,480 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + + +class InputController: + """Base class for input controllers that generate motion deltas.""" + + def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0): + """ + Initialize the controller. + + Args: + x_step_size: Base movement step size in meters + y_step_size: Base movement step size in meters + z_step_size: Base movement step size in meters + """ + self.x_step_size = x_step_size + self.y_step_size = y_step_size + self.z_step_size = z_step_size + self.running = True + self.episode_end_status = None # None, "success", or "failure" + self.intervention_flag = False + self.open_gripper_command = False + self.close_gripper_command = False + + def start(self): + """Start the controller and initialize resources.""" + pass + + def stop(self): + """Stop the controller and release resources.""" + pass + + def get_deltas(self): + """Get the current movement deltas (dx, dy, dz) in meters.""" + return 0.0, 0.0, 0.0 + + def should_quit(self): + """Return True if the user has requested to quit.""" + return not self.running + + def update(self): + """Update controller state - call this once per frame.""" + pass + + def __enter__(self): + """Support for use in 'with' statements.""" + self.start() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """Ensure resources are released when exiting 'with' block.""" + self.stop() + + def get_episode_end_status(self): + """ + Get the current episode end status. + + Returns: + None if episode should continue, "success" or "failure" otherwise + """ + status = self.episode_end_status + self.episode_end_status = None # Reset after reading + return status + + def should_intervene(self): + """Return True if intervention flag was set.""" + return self.intervention_flag + + def gripper_command(self): + """Return the current gripper command.""" + if self.open_gripper_command == self.close_gripper_command: + return "stay" + elif self.open_gripper_command: + return "open" + elif self.close_gripper_command: + return "close" + + +class KeyboardController(InputController): + """Generate motion deltas from keyboard input.""" + + def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0): + super().__init__(x_step_size, y_step_size, z_step_size) + self.key_states = { + "forward_x": False, + "backward_x": False, + "forward_y": False, + "backward_y": False, + "forward_z": False, + "backward_z": False, + "quit": False, + "success": False, + "failure": False, + } + self.listener = None + + def start(self): + """Start the keyboard listener.""" + from pynput import keyboard + + def on_press(key): + try: + if key == keyboard.Key.up: + self.key_states["forward_x"] = True + elif key == keyboard.Key.down: + self.key_states["backward_x"] = True + elif key == keyboard.Key.left: + self.key_states["forward_y"] = True + elif key == keyboard.Key.right: + self.key_states["backward_y"] = True + elif key == keyboard.Key.shift: + self.key_states["backward_z"] = True + elif key == keyboard.Key.shift_r: + self.key_states["forward_z"] = True + elif key == keyboard.Key.esc: + self.key_states["quit"] = True + self.running = False + return False + elif key == keyboard.Key.enter: + self.key_states["success"] = True + self.episode_end_status = "success" + elif key == keyboard.Key.backspace: + self.key_states["failure"] = True + self.episode_end_status = "failure" + except AttributeError: + pass + + def on_release(key): + try: + if key == keyboard.Key.up: + self.key_states["forward_x"] = False + elif key == keyboard.Key.down: + self.key_states["backward_x"] = False + elif key == keyboard.Key.left: + self.key_states["forward_y"] = False + elif key == keyboard.Key.right: + self.key_states["backward_y"] = False + elif key == keyboard.Key.shift: + self.key_states["backward_z"] = False + elif key == keyboard.Key.shift_r: + self.key_states["forward_z"] = False + elif key == keyboard.Key.enter: + self.key_states["success"] = False + elif key == keyboard.Key.backspace: + self.key_states["failure"] = False + except AttributeError: + pass + + self.listener = keyboard.Listener(on_press=on_press, on_release=on_release) + self.listener.start() + + print("Keyboard controls:") + print(" Arrow keys: Move in X-Y plane") + print(" Shift and Shift_R: Move in Z axis") + print(" Enter: End episode with SUCCESS") + print(" Backspace: End episode with FAILURE") + print(" ESC: Exit") + + def stop(self): + """Stop the keyboard listener.""" + if self.listener and self.listener.is_alive(): + self.listener.stop() + + def get_deltas(self): + """Get the current movement deltas from keyboard state.""" + delta_x = delta_y = delta_z = 0.0 + + if self.key_states["forward_x"]: + delta_x += self.x_step_size + if self.key_states["backward_x"]: + delta_x -= self.x_step_size + if self.key_states["forward_y"]: + delta_y += self.y_step_size + if self.key_states["backward_y"]: + delta_y -= self.y_step_size + if self.key_states["forward_z"]: + delta_z += self.z_step_size + if self.key_states["backward_z"]: + delta_z -= self.z_step_size + + return delta_x, delta_y, delta_z + + def should_quit(self): + """Return True if ESC was pressed.""" + return self.key_states["quit"] + + def should_save(self): + """Return True if Enter was pressed (save episode).""" + return self.key_states["success"] or self.key_states["failure"] + + +class GamepadController(InputController): + """Generate motion deltas from gamepad input.""" + + def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0, deadzone=0.1): + super().__init__(x_step_size, y_step_size, z_step_size) + self.deadzone = deadzone + self.joystick = None + self.intervention_flag = False + + def start(self): + """Initialize pygame and the gamepad.""" + import pygame + + pygame.init() + pygame.joystick.init() + + if pygame.joystick.get_count() == 0: + logging.error("No gamepad detected. Please connect a gamepad and try again.") + self.running = False + return + + self.joystick = pygame.joystick.Joystick(0) + self.joystick.init() + logging.info(f"Initialized gamepad: {self.joystick.get_name()}") + + print("Gamepad controls:") + print(" Left analog stick: Move in X-Y plane") + print(" Right analog stick (vertical): Move in Z axis") + print(" B/Circle button: Exit") + print(" Y/Triangle button: End episode with SUCCESS") + print(" A/Cross button: End episode with FAILURE") + print(" X/Square button: Rerecord episode") + + def stop(self): + """Clean up pygame resources.""" + import pygame + + if pygame.joystick.get_init(): + if self.joystick: + self.joystick.quit() + pygame.joystick.quit() + pygame.quit() + + def update(self): + """Process pygame events to get fresh gamepad readings.""" + import pygame + + for event in pygame.event.get(): + if event.type == pygame.JOYBUTTONDOWN: + if event.button == 3: + self.episode_end_status = "success" + # A button (1) for failure + elif event.button == 1: + self.episode_end_status = "failure" + # X button (0) for rerecord + elif event.button == 0: + self.episode_end_status = "rerecord_episode" + + # RB button (6) for closing gripper + elif event.button == 6: + self.close_gripper_command = True + + # LT button (7) for opening gripper + elif event.button == 7: + self.open_gripper_command = True + + # Reset episode status on button release + elif event.type == pygame.JOYBUTTONUP: + if event.button in [0, 2, 3]: + self.episode_end_status = None + + elif event.button == 6: + self.close_gripper_command = False + + elif event.button == 7: + self.open_gripper_command = False + + # Check for RB button (typically button 5) for intervention flag + if self.joystick.get_button(5): + self.intervention_flag = True + else: + self.intervention_flag = False + + def get_deltas(self): + """Get the current movement deltas from gamepad state.""" + import pygame + + try: + # Read joystick axes + # Left stick X and Y (typically axes 0 and 1) + x_input = self.joystick.get_axis(0) # Left/Right + y_input = self.joystick.get_axis(1) # Up/Down (often inverted) + + # Right stick Y (typically axis 3 or 4) + z_input = self.joystick.get_axis(3) # Up/Down for Z + + # Apply deadzone to avoid drift + x_input = 0 if abs(x_input) < self.deadzone else x_input + y_input = 0 if abs(y_input) < self.deadzone else y_input + z_input = 0 if abs(z_input) < self.deadzone else z_input + + # Calculate deltas (note: may need to invert axes depending on controller) + delta_x = -y_input * self.y_step_size # Forward/backward + delta_y = -x_input * self.x_step_size # Left/right + delta_z = -z_input * self.z_step_size # Up/down + + return delta_x, delta_y, delta_z + + except pygame.error: + logging.error("Error reading gamepad. Is it still connected?") + return 0.0, 0.0, 0.0 + + +class GamepadControllerHID(InputController): + """Generate motion deltas from gamepad input using HIDAPI.""" + + def __init__( + self, + x_step_size=1.0, + y_step_size=1.0, + z_step_size=1.0, + deadzone=0.1, + ): + """ + Initialize the HID gamepad controller. + + Args: + step_size: Base movement step size in meters + z_scale: Scaling factor for Z-axis movement + deadzone: Joystick deadzone to prevent drift + """ + super().__init__(x_step_size, y_step_size, z_step_size) + self.deadzone = deadzone + self.device = None + self.device_info = None + + # Movement values (normalized from -1.0 to 1.0) + self.left_x = 0.0 + self.left_y = 0.0 + self.right_x = 0.0 + self.right_y = 0.0 + + # Button states + self.buttons = {} + self.quit_requested = False + self.save_requested = False + + def find_device(self): + """Look for the gamepad device by vendor and product ID.""" + import hid + + devices = hid.enumerate() + for device in devices: + device_name = device["product_string"] + if any(controller in device_name for controller in ["Logitech", "Xbox", "PS4", "PS5"]): + return device + + logging.error( + "No gamepad found, check the connection and the product string in HID to add your gamepad" + ) + return None + + def start(self): + """Connect to the gamepad using HIDAPI.""" + import hid + + self.device_info = self.find_device() + if not self.device_info: + self.running = False + return + + try: + logging.info(f"Connecting to gamepad at path: {self.device_info['path']}") + self.device = hid.device() + self.device.open_path(self.device_info["path"]) + self.device.set_nonblocking(1) + + manufacturer = self.device.get_manufacturer_string() + product = self.device.get_product_string() + logging.info(f"Connected to {manufacturer} {product}") + + logging.info("Gamepad controls (HID mode):") + logging.info(" Left analog stick: Move in X-Y plane") + logging.info(" Right analog stick: Move in Z axis (vertical)") + logging.info(" Button 1/B/Circle: Exit") + logging.info(" Button 2/A/Cross: End episode with SUCCESS") + logging.info(" Button 3/X/Square: End episode with FAILURE") + + except OSError as e: + logging.error(f"Error opening gamepad: {e}") + logging.error("You might need to run this with sudo/admin privileges on some systems") + self.running = False + + def stop(self): + """Close the HID device connection.""" + if self.device: + self.device.close() + self.device = None + + def update(self): + """ + Read and process the latest gamepad data. + Due to an issue with the HIDAPI, we need to read the read the device several times in order to get a stable reading + """ + for _ in range(10): + self._update() + + def _update(self): + """Read and process the latest gamepad data.""" + if not self.device or not self.running: + return + + try: + # Read data from the gamepad + data = self.device.read(64) + # Interpret gamepad data - this will vary by controller model + # These offsets are for the Logitech RumblePad 2 + if data and len(data) >= 8: + # Normalize joystick values from 0-255 to -1.0-1.0 + self.left_x = (data[1] - 128) / 128.0 + self.left_y = (data[2] - 128) / 128.0 + self.right_x = (data[3] - 128) / 128.0 + self.right_y = (data[4] - 128) / 128.0 + + # Apply deadzone + self.left_x = 0 if abs(self.left_x) < self.deadzone else self.left_x + self.left_y = 0 if abs(self.left_y) < self.deadzone else self.left_y + self.right_x = 0 if abs(self.right_x) < self.deadzone else self.right_x + self.right_y = 0 if abs(self.right_y) < self.deadzone else self.right_y + + # Parse button states (byte 5 in the Logitech RumblePad 2) + buttons = data[5] + + # Check if RB is pressed then the intervention flag should be set + self.intervention_flag = data[6] in [2, 6, 10, 14] + + # Check if RT is pressed + self.open_gripper_command = data[6] in [8, 10, 12] + + # Check if LT is pressed + self.close_gripper_command = data[6] in [4, 6, 12] + + # Check if Y/Triangle button (bit 7) is pressed for saving + # Check if X/Square button (bit 5) is pressed for failure + # Check if A/Cross button (bit 4) is pressed for rerecording + if buttons & 1 << 7: + self.episode_end_status = "success" + elif buttons & 1 << 5: + self.episode_end_status = "failure" + elif buttons & 1 << 4: + self.episode_end_status = "rerecord_episode" + else: + self.episode_end_status = None + + except OSError as e: + logging.error(f"Error reading from gamepad: {e}") + + def get_deltas(self): + """Get the current movement deltas from gamepad state.""" + # Calculate deltas - invert as needed based on controller orientation + delta_x = -self.left_y * self.x_step_size # Forward/backward + delta_y = -self.left_x * self.y_step_size # Left/right + delta_z = -self.right_y * self.z_step_size # Up/down + + return delta_x, delta_y, delta_z + + def should_quit(self): + """Return True if quit button was pressed.""" + return self.quit_requested + + def should_save(self): + """Return True if save button was pressed.""" + return self.save_requested diff --git a/lerobot/common/teleoperators/gamepad/teleop_gamepad.py b/lerobot/common/teleoperators/gamepad/teleop_gamepad.py new file mode 100644 index 0000000000000000000000000000000000000000..251e2b3b84b46e7965ebf9a74cfd14fc250e14ea --- /dev/null +++ b/lerobot/common/teleoperators/gamepad/teleop_gamepad.py @@ -0,0 +1,138 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys +from enum import IntEnum +from typing import Any + +import numpy as np + +from ..teleoperator import Teleoperator +from .configuration_gamepad import GamepadTeleopConfig + + +class GripperAction(IntEnum): + CLOSE = 0 + STAY = 1 + OPEN = 2 + + +gripper_action_map = { + "close": GripperAction.CLOSE.value, + "open": GripperAction.OPEN.value, + "stay": GripperAction.STAY.value, +} + + +class GamepadTeleop(Teleoperator): + """ + Teleop class to use gamepad inputs for control. + """ + + config_class = GamepadTeleopConfig + name = "gamepad" + + def __init__(self, config: GamepadTeleopConfig): + super().__init__(config) + self.config = config + self.robot_type = config.type + + self.gamepad = None + + @property + def action_features(self) -> dict: + if self.config.use_gripper: + return { + "dtype": "float32", + "shape": (4,), + "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2, "gripper": 3}, + } + else: + return { + "dtype": "float32", + "shape": (3,), + "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2}, + } + + @property + def feedback_features(self) -> dict: + return {} + + def connect(self) -> None: + # use HidApi for macos + if sys.platform == "darwin": + # NOTE: On macOS, pygame doesn’t reliably detect input from some controllers so we fall back to hidapi + from .gamepad_utils import GamepadControllerHID as Gamepad + else: + from .gamepad_utils import GamepadController as Gamepad + + self.gamepad = Gamepad() + self.gamepad.start() + + def get_action(self) -> dict[str, Any]: + # Update the controller to get fresh inputs + self.gamepad.update() + + # Get movement deltas from the controller + delta_x, delta_y, delta_z = self.gamepad.get_deltas() + + # Create action from gamepad input + gamepad_action = np.array([delta_x, delta_y, delta_z], dtype=np.float32) + + action_dict = { + "delta_x": gamepad_action[0], + "delta_y": gamepad_action[1], + "delta_z": gamepad_action[2], + } + + # Default gripper action is to stay + gripper_action = GripperAction.STAY.value + if self.config.use_gripper: + gripper_command = self.gamepad.gripper_command() + gripper_action = gripper_action_map[gripper_command] + action_dict["gripper"] = gripper_action + + return action_dict + + def disconnect(self) -> None: + """Disconnect from the gamepad.""" + if self.gamepad is not None: + self.gamepad.stop() + self.gamepad = None + + def is_connected(self) -> bool: + """Check if gamepad is connected.""" + return self.gamepad is not None + + def calibrate(self) -> None: + """Calibrate the gamepad.""" + # No calibration needed for gamepad + pass + + def is_calibrated(self) -> bool: + """Check if gamepad is calibrated.""" + # Gamepad doesn't require calibration + return True + + def configure(self) -> None: + """Configure the gamepad.""" + # No additional configuration needed + pass + + def send_feedback(self, feedback: dict) -> None: + """Send feedback to the gamepad.""" + # Gamepad doesn't support feedback + pass diff --git a/lerobot/common/teleoperators/keyboard/__init__.py b/lerobot/common/teleoperators/keyboard/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8d88c12e99caed4d3fe64b76e219854221bcacdc --- /dev/null +++ b/lerobot/common/teleoperators/keyboard/__init__.py @@ -0,0 +1,4 @@ +from .configuration_keyboard import KeyboardTeleopConfig +from .teleop_keyboard import KeyboardTeleop + +__all__ = ["KeyboardTeleopConfig", "KeyboardTeleop"] diff --git a/lerobot/common/teleoperators/keyboard/configuration_keyboard.py b/lerobot/common/teleoperators/keyboard/configuration_keyboard.py new file mode 100644 index 0000000000000000000000000000000000000000..30796a492c0f9214bccdd0248042979ee94bd4bf --- /dev/null +++ b/lerobot/common/teleoperators/keyboard/configuration_keyboard.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("keyboard") +@dataclass +class KeyboardTeleopConfig(TeleoperatorConfig): + # TODO(Steven): Consider setting in here the keys that we want to capture/listen + mock: bool = False diff --git a/lerobot/common/teleoperators/keyboard/teleop_keyboard.py b/lerobot/common/teleoperators/keyboard/teleop_keyboard.py new file mode 100644 index 0000000000000000000000000000000000000000..bfe017de611da859dd013d692b117b721d906f66 --- /dev/null +++ b/lerobot/common/teleoperators/keyboard/teleop_keyboard.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import sys +import time +from queue import Queue +from typing import Any + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..teleoperator import Teleoperator +from .configuration_keyboard import KeyboardTeleopConfig + +PYNPUT_AVAILABLE = True +try: + if ("DISPLAY" not in os.environ) and ("linux" in sys.platform): + logging.info("No DISPLAY set. Skipping pynput import.") + raise ImportError("pynput blocked intentionally due to no display.") + + from pynput import keyboard +except ImportError: + keyboard = None + PYNPUT_AVAILABLE = False +except Exception as e: + keyboard = None + PYNPUT_AVAILABLE = False + logging.info(f"Could not import pynput: {e}") + + +class KeyboardTeleop(Teleoperator): + """ + Teleop class to use keyboard inputs for control. + """ + + config_class = KeyboardTeleopConfig + name = "keyboard" + + def __init__(self, config: KeyboardTeleopConfig): + super().__init__(config) + self.config = config + self.robot_type = config.type + + self.event_queue = Queue() + self.current_pressed = {} + self.listener = None + self.logs = {} + + @property + def action_features(self) -> dict: + return { + "dtype": "float32", + "shape": (len(self.arm),), + "names": {"motors": list(self.arm.motors)}, + } + + @property + def feedback_features(self) -> dict: + return {} + + @property + def is_connected(self) -> bool: + return PYNPUT_AVAILABLE and isinstance(self.listener, keyboard.Listener) and self.listener.is_alive() + + @property + def is_calibrated(self) -> bool: + pass + + def connect(self) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError( + "Keyboard is already connected. Do not run `robot.connect()` twice." + ) + + if PYNPUT_AVAILABLE: + logging.info("pynput is available - enabling local keyboard listener.") + self.listener = keyboard.Listener( + on_press=self._on_press, + on_release=self._on_release, + ) + self.listener.start() + else: + logging.info("pynput not available - skipping local keyboard listener.") + self.listener = None + + def calibrate(self) -> None: + pass + + def _on_press(self, key): + if hasattr(key, "char"): + self.event_queue.put((key.char, True)) + + def _on_release(self, key): + if hasattr(key, "char"): + self.event_queue.put((key.char, False)) + if key == keyboard.Key.esc: + logging.info("ESC pressed, disconnecting.") + self.disconnect() + + def _drain_pressed_keys(self): + while not self.event_queue.empty(): + key_char, is_pressed = self.event_queue.get_nowait() + self.current_pressed[key_char] = is_pressed + + def configure(self): + pass + + def get_action(self) -> dict[str, Any]: + before_read_t = time.perf_counter() + + if not self.is_connected: + raise DeviceNotConnectedError( + "KeyboardTeleop is not connected. You need to run `connect()` before `get_action()`." + ) + + self._drain_pressed_keys() + + # Generate action based on current key states + action = {key for key, val in self.current_pressed.items() if val} + self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t + + return dict.fromkeys(action, None) + + def send_feedback(self, feedback: dict[str, Any]) -> None: + pass + + def disconnect(self) -> None: + if not self.is_connected: + raise DeviceNotConnectedError( + "KeyboardTeleop is not connected. You need to run `robot.connect()` before `disconnect()`." + ) + if self.listener is not None: + self.listener.stop() diff --git a/lerobot/common/teleoperators/koch_leader/__init__.py b/lerobot/common/teleoperators/koch_leader/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ef6b0b80de5d9d502f466ee0582f9d7108c180c7 --- /dev/null +++ b/lerobot/common/teleoperators/koch_leader/__init__.py @@ -0,0 +1,2 @@ +from .config_koch_leader import KochLeaderConfig +from .koch_leader import KochLeader diff --git a/lerobot/common/teleoperators/koch_leader/config_koch_leader.py b/lerobot/common/teleoperators/koch_leader/config_koch_leader.py new file mode 100644 index 0000000000000000000000000000000000000000..d8023c910ce8f0a1a42c1630d7b987f145239719 --- /dev/null +++ b/lerobot/common/teleoperators/koch_leader/config_koch_leader.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("koch_leader") +@dataclass +class KochLeaderConfig(TeleoperatorConfig): + # Port to connect to the arm + port: str + + # Sets the arm in torque mode with the gripper motor set to this value. This makes it possible to squeeze + # the gripper and have it spring back to an open position on its own. + gripper_open_pos: float = 50.0 diff --git a/lerobot/common/teleoperators/koch_leader/koch_leader.py b/lerobot/common/teleoperators/koch_leader/koch_leader.py new file mode 100644 index 0000000000000000000000000000000000000000..560a7fed14c295a6b477b4a1c4c20aa7b67d5f7c --- /dev/null +++ b/lerobot/common/teleoperators/koch_leader/koch_leader.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.dynamixel import ( + DriveMode, + DynamixelMotorsBus, + OperatingMode, +) + +from ..teleoperator import Teleoperator +from .config_koch_leader import KochLeaderConfig + +logger = logging.getLogger(__name__) + + +class KochLeader(Teleoperator): + """ + - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow + expansion, developed by Alexander Koch from [Tau Robotics](https://tau-robotics.com) + - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss + """ + + config_class = KochLeaderConfig + name = "koch_leader" + + def __init__(self, config: KochLeaderConfig): + super().__init__(config) + self.config = config + self.bus = DynamixelMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "xl330-m077", MotorNormMode.RANGE_M100_100), + "shoulder_lift": Motor(2, "xl330-m077", MotorNormMode.RANGE_M100_100), + "elbow_flex": Motor(3, "xl330-m077", MotorNormMode.RANGE_M100_100), + "wrist_flex": Motor(4, "xl330-m077", MotorNormMode.RANGE_M100_100), + "wrist_roll": Motor(5, "xl330-m077", MotorNormMode.RANGE_M100_100), + "gripper": Motor(6, "xl330-m077", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + + @property + def action_features(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def feedback_features(self) -> dict[str, type]: + return {} + + @property + def is_connected(self) -> bool: + return self.bus.is_connected + + def connect(self, calibrate: bool = True) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + self.bus.write("Drive_Mode", "elbow_flex", DriveMode.INVERTED.value) + drive_modes = {motor: 1 if motor == "elbow_flex" else 0 for motor in self.bus.motors} + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + full_turn_motors = ["shoulder_pan", "wrist_roll"] + unknown_range_motors = [motor for motor in self.bus.motors if motor not in full_turn_motors] + print( + f"Move all joints except {full_turn_motors} sequentially through their " + "entire ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + for motor in full_turn_motors: + range_mins[motor] = 0 + range_maxes[motor] = 4095 + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=drive_modes[motor], + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + logger.info(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + self.bus.disable_torque() + self.bus.configure_motors() + for motor in self.bus.motors: + if motor != "gripper": + # Use 'extended position mode' for all motors except gripper, because in joint mode the servos + # can't rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while + # assembling the arm, you could end up with a servo with a position 0 or 4095 at a crucial + # point + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + # Use 'position control current based' for gripper to be limited by the limit of the current. + # For the follower gripper, it means it can grasp an object without forcing too much even tho, + # its goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch). + # For the leader gripper, it means we can use it as a physical trigger, since we can force with our finger + # to make it move, and it will move back to its original target position when we release the force. + self.bus.write("Operating_Mode", "gripper", OperatingMode.CURRENT_POSITION.value) + # Set gripper's goal pos in current position mode so that we can use it as a trigger. + self.bus.enable_torque("gripper") + if self.is_calibrated: + self.bus.write("Goal_Position", "gripper", self.config.gripper_open_pos) + + def setup_motors(self) -> None: + for motor in reversed(self.bus.motors): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + def get_action(self) -> dict[str, float]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + start = time.perf_counter() + action = self.bus.sync_read("Present_Position") + action = {f"{motor}.pos": val for motor, val in action.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read action: {dt_ms:.1f}ms") + return action + + def send_feedback(self, feedback: dict[str, float]) -> None: + # TODO(rcadene, aliberts): Implement force feedback + raise NotImplementedError + + def disconnect(self) -> None: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect() + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/teleoperators/so100_leader/__init__.py b/lerobot/common/teleoperators/so100_leader/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bdb4227ce36b8db1c7eb7092895db2a593238219 --- /dev/null +++ b/lerobot/common/teleoperators/so100_leader/__init__.py @@ -0,0 +1,2 @@ +from .config_so100_leader import SO100LeaderConfig +from .so100_leader import SO100Leader diff --git a/lerobot/common/teleoperators/so100_leader/config_so100_leader.py b/lerobot/common/teleoperators/so100_leader/config_so100_leader.py new file mode 100644 index 0000000000000000000000000000000000000000..185c238cff74bed4dbefac0f39a041c7b9262d58 --- /dev/null +++ b/lerobot/common/teleoperators/so100_leader/config_so100_leader.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("so100_leader") +@dataclass +class SO100LeaderConfig(TeleoperatorConfig): + # Port to connect to the arm + port: str diff --git a/lerobot/common/teleoperators/so100_leader/so100_leader.py b/lerobot/common/teleoperators/so100_leader/so100_leader.py new file mode 100644 index 0000000000000000000000000000000000000000..54ba79d472a6f986b7d3e982c0fa27f1e9dfa4bf --- /dev/null +++ b/lerobot/common/teleoperators/so100_leader/so100_leader.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.feetech import ( + FeetechMotorsBus, + OperatingMode, +) + +from ..teleoperator import Teleoperator +from .config_so100_leader import SO100LeaderConfig + +logger = logging.getLogger(__name__) + + +class SO100Leader(Teleoperator): + """ + [SO-100 Leader Arm](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio + """ + + config_class = SO100LeaderConfig + name = "so100_leader" + + def __init__(self, config: SO100LeaderConfig): + super().__init__(config) + self.config = config + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "sts3215", MotorNormMode.RANGE_M100_100), + "shoulder_lift": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100), + "elbow_flex": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100), + "wrist_flex": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100), + "wrist_roll": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100), + "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + + @property + def action_features(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def feedback_features(self) -> dict[str, type]: + return {} + + @property + def is_connected(self) -> bool: + return self.bus.is_connected + + def connect(self, calibrate: bool = True) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + full_turn_motor = "wrist_roll" + unknown_range_motors = [motor for motor in self.bus.motors if motor != full_turn_motor] + print( + f"Move all joints except '{full_turn_motor}' sequentially through their " + "entire ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + range_mins[full_turn_motor] = 0 + range_maxes[full_turn_motor] = 4095 + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + self.bus.disable_torque() + self.bus.configure_motors() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + def setup_motors(self) -> None: + for motor in reversed(self.bus.motors): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + def get_action(self) -> dict[str, float]: + start = time.perf_counter() + action = self.bus.sync_read("Present_Position") + action = {f"{motor}.pos": val for motor, val in action.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read action: {dt_ms:.1f}ms") + return action + + def send_feedback(self, feedback: dict[str, float]) -> None: + # TODO(rcadene, aliberts): Implement force feedback + raise NotImplementedError + + def disconnect(self) -> None: + if not self.is_connected: + DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect() + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/teleoperators/so101_leader/__init__.py b/lerobot/common/teleoperators/so101_leader/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4bee6f7f2fff3acc26aa47add25a9fd2158b3b --- /dev/null +++ b/lerobot/common/teleoperators/so101_leader/__init__.py @@ -0,0 +1,2 @@ +from .config_so101_leader import SO101LeaderConfig +from .so101_leader import SO101Leader diff --git a/lerobot/common/teleoperators/so101_leader/config_so101_leader.py b/lerobot/common/teleoperators/so101_leader/config_so101_leader.py new file mode 100644 index 0000000000000000000000000000000000000000..2b622ae27a96e5e39fdd596a7aa870f18774e40e --- /dev/null +++ b/lerobot/common/teleoperators/so101_leader/config_so101_leader.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("so101_leader") +@dataclass +class SO101LeaderConfig(TeleoperatorConfig): + # Port to connect to the arm + port: str + + use_degrees: bool = False diff --git a/lerobot/common/teleoperators/so101_leader/so101_leader.py b/lerobot/common/teleoperators/so101_leader/so101_leader.py new file mode 100644 index 0000000000000000000000000000000000000000..f2a673a74aa984ff7dfeac95663d4c387935a874 --- /dev/null +++ b/lerobot/common/teleoperators/so101_leader/so101_leader.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.feetech import ( + FeetechMotorsBus, + OperatingMode, +) + +from ..teleoperator import Teleoperator +from .config_so101_leader import SO101LeaderConfig + +logger = logging.getLogger(__name__) + + +class SO101Leader(Teleoperator): + """ + SO-101 Leader Arm designed by TheRobotStudio and Hugging Face. + """ + + config_class = SO101LeaderConfig + name = "so101_leader" + + def __init__(self, config: SO101LeaderConfig): + super().__init__(config) + self.config = config + norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100 + self.bus = FeetechMotorsBus( + port=self.config.port, + motors={ + "shoulder_pan": Motor(1, "sts3215", norm_mode_body), + "shoulder_lift": Motor(2, "sts3215", norm_mode_body), + "elbow_flex": Motor(3, "sts3215", norm_mode_body), + "wrist_flex": Motor(4, "sts3215", norm_mode_body), + "wrist_roll": Motor(5, "sts3215", norm_mode_body), + "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), + }, + calibration=self.calibration, + ) + + @property + def action_features(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def feedback_features(self) -> dict[str, type]: + return {} + + @property + def is_connected(self) -> bool: + return self.bus.is_connected + + def connect(self, calibrate: bool = True) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + input(f"Move {self} to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + print( + "Move all joints sequentially through their entire ranges " + "of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion() + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=0, + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + self.bus.disable_torque() + self.bus.configure_motors() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value) + + def setup_motors(self) -> None: + for motor in reversed(self.bus.motors): + input(f"Connect the controller board to the '{motor}' motor only and press enter.") + self.bus.setup_motor(motor) + print(f"'{motor}' motor id set to {self.bus.motors[motor].id}") + + def get_action(self) -> dict[str, float]: + start = time.perf_counter() + action = self.bus.sync_read("Present_Position") + action = {f"{motor}.pos": val for motor, val in action.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read action: {dt_ms:.1f}ms") + return action + + def send_feedback(self, feedback: dict[str, float]) -> None: + # TODO(rcadene, aliberts): Implement force feedback + raise NotImplementedError + + def disconnect(self) -> None: + if not self.is_connected: + DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect() + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/teleoperators/stretch3_gamepad/__init__.py b/lerobot/common/teleoperators/stretch3_gamepad/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4306b46fbf23bf415da7180f65c72dc96d2b8468 --- /dev/null +++ b/lerobot/common/teleoperators/stretch3_gamepad/__init__.py @@ -0,0 +1,2 @@ +from .configuration_stretch3 import Stretch3GamePadConfig +from .stretch3_gamepad import Stretch3GamePad diff --git a/lerobot/common/teleoperators/stretch3_gamepad/configuration_stretch3.py b/lerobot/common/teleoperators/stretch3_gamepad/configuration_stretch3.py new file mode 100644 index 0000000000000000000000000000000000000000..5caf23d3a0eafdcaa132a87c78a961008469fe6a --- /dev/null +++ b/lerobot/common/teleoperators/stretch3_gamepad/configuration_stretch3.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("stretch3") +@dataclass +class Stretch3GamePadConfig(TeleoperatorConfig): + mock: bool = False diff --git a/lerobot/common/teleoperators/stretch3_gamepad/stretch3_gamepad.py b/lerobot/common/teleoperators/stretch3_gamepad/stretch3_gamepad.py new file mode 100644 index 0000000000000000000000000000000000000000..d899aebb556072a8afd7fb27fcfa08a679a857ab --- /dev/null +++ b/lerobot/common/teleoperators/stretch3_gamepad/stretch3_gamepad.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time + +import numpy as np +from stretch_body.gamepad_teleop import GamePadTeleop +from stretch_body.robot_params import RobotParams + +from lerobot.common.errors import DeviceAlreadyConnectedError + +from ..teleoperator import Teleoperator +from .configuration_stretch3 import Stretch3GamePadConfig + +# from stretch_body.gamepad_controller.GamePadController +GAMEPAD_BUTTONS = [ + "middle_led_ring_button_pressed", + "left_stick_x", + "left_stick_y", + "right_stick_x", + "right_stick_y", + "left_stick_button_pressed", + "right_stick_button_pressed", + "bottom_button_pressed", + "top_button_pressed", + "left_button_pressed", + "right_button_pressed", + "left_shoulder_button_pressed", + "right_shoulder_button_pressed", + "select_button_pressed", + "start_button_pressed", + "left_trigger_pulled", + "right_trigger_pulled", + "bottom_pad_pressed", + "top_pad_pressed", + "left_pad_pressed", + "right_pad_pressed", +] + + +class Stretch3GamePad(Teleoperator): + """[Stretch 3](https://hello-robot.com/stretch-3-product), by Hello Robot.""" + + config_class = Stretch3GamePadConfig + name = "stretch3" + + def __init__(self, config: Stretch3GamePadConfig): + raise NotImplementedError + super().__init__(config) + + self.config = config + self.robot_type = self.config.type + + self.api = GamePadTeleop(robot_instance=False) + + self.is_connected = False + self.logs = {} + + # TODO(aliberts): test this + RobotParams.set_logging_level("WARNING") + RobotParams.set_logging_formatter("brief_console_formatter") + + @property + def action_features(self) -> dict: + return { + "dtype": "float32", + "shape": (len(GAMEPAD_BUTTONS),), + "names": {"buttons": GAMEPAD_BUTTONS}, + } + + @property + def feedback_features(self) -> dict: + return {} + + def connect(self) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError( + "ManipulatorRobot is already connected. Do not run `robot.connect()` twice." + ) + + self.api.startup() + self.api._update_state() # Check controller can be read & written + self.api._update_modes() + self.is_connected = True + + def calibrate(self) -> None: + pass + + def get_action(self) -> np.ndarray: + # Read Stretch state + before_read_t = time.perf_counter() + action = self.api.gamepad_controller.get_state() + self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t + + action = np.asarray(list(action.values())) + + return action + + def send_feedback(self, feedback: np.ndarray) -> None: + pass + + def print_logs(self) -> None: + pass + # TODO(aliberts): move robot-specific logs logic here + + def disconnect(self) -> None: + self.api.stop() + self.is_connected = False diff --git a/lerobot/common/teleoperators/teleoperator.py b/lerobot/common/teleoperators/teleoperator.py new file mode 100644 index 0000000000000000000000000000000000000000..aefc432a95a9a17b2270f1de7127a6da00afac8b --- /dev/null +++ b/lerobot/common/teleoperators/teleoperator.py @@ -0,0 +1,180 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import abc +from pathlib import Path +from typing import Any + +import draccus + +from lerobot.common.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS +from lerobot.common.motors.motors_bus import MotorCalibration + +from .config import TeleoperatorConfig + + +class Teleoperator(abc.ABC): + """ + The base abstract class for all LeRobot-compatible teleoperation devices. + + This class provides a standardized interface for interacting with physical teleoperators. + Subclasses must implement all abstract methods and properties to be usable. + + Attributes: + config_class (RobotConfig): The expected configuration class for this teleoperator. + name (str): The unique name used to identify this teleoperator type. + """ + + # Set these in ALL subclasses + config_class: TeleoperatorConfig + name: str + + def __init__(self, config: TeleoperatorConfig): + self.id = config.id + self.calibration_dir = ( + config.calibration_dir + if config.calibration_dir + else HF_LEROBOT_CALIBRATION / TELEOPERATORS / self.name + ) + self.calibration_dir.mkdir(parents=True, exist_ok=True) + self.calibration_fpath = self.calibration_dir / f"{self.id}.json" + self.calibration: dict[str, MotorCalibration] = {} + if self.calibration_fpath.is_file(): + self._load_calibration() + + def __str__(self) -> str: + return f"{self.id} {self.__class__.__name__}" + + @property + @abc.abstractmethod + def action_features(self) -> dict: + """ + A dictionary describing the structure and types of the actions produced by the teleoperator. Its + structure (keys) should match the structure of what is returned by :pymeth:`get_action`. Values for + the dict should be the type of the value if it's a simple value, e.g. `float` for single + proprioceptive value (a joint's goal position/velocity) + + Note: this property should be able to be called regardless of whether the robot is connected or not. + """ + pass + + @property + @abc.abstractmethod + def feedback_features(self) -> dict: + """ + A dictionary describing the structure and types of the feedback actions expected by the robot. Its + structure (keys) should match the structure of what is passed to :pymeth:`send_feedback`. Values for + the dict should be the type of the value if it's a simple value, e.g. `float` for single + proprioceptive value (a joint's goal position/velocity) + + Note: this property should be able to be called regardless of whether the robot is connected or not. + """ + pass + + @property + @abc.abstractmethod + def is_connected(self) -> bool: + """ + Whether the teleoperator is currently connected or not. If `False`, calling :pymeth:`get_action` + or :pymeth:`send_feedback` should raise an error. + """ + pass + + @abc.abstractmethod + def connect(self, calibrate: bool = True) -> None: + """ + Establish communication with the teleoperator. + + Args: + calibrate (bool): If True, automatically calibrate the teleoperator after connecting if it's not + calibrated or needs calibration (this is hardware-dependant). + """ + pass + + @property + @abc.abstractmethod + def is_calibrated(self) -> bool: + """Whether the teleoperator is currently calibrated or not. Should be always `True` if not applicable""" + pass + + @abc.abstractmethod + def calibrate(self) -> None: + """ + Calibrate the teleoperator if applicable. If not, this should be a no-op. + + This method should collect any necessary data (e.g., motor offsets) and update the + :pyattr:`calibration` dictionary accordingly. + """ + pass + + def _load_calibration(self, fpath: Path | None = None) -> None: + """ + Helper to load calibration data from the specified file. + + Args: + fpath (Path | None): Optional path to the calibration file. Defaults to `self.calibration_fpath`. + """ + fpath = self.calibration_fpath if fpath is None else fpath + with open(fpath) as f, draccus.config_type("json"): + self.calibration = draccus.load(dict[str, MotorCalibration], f) + + def _save_calibration(self, fpath: Path | None = None) -> None: + """ + Helper to save calibration data to the specified file. + + Args: + fpath (Path | None): Optional path to save the calibration file. Defaults to `self.calibration_fpath`. + """ + fpath = self.calibration_fpath if fpath is None else fpath + with open(fpath, "w") as f, draccus.config_type("json"): + draccus.dump(self.calibration, f, indent=4) + + @abc.abstractmethod + def configure(self) -> None: + """ + Apply any one-time or runtime configuration to the teleoperator. + This may include setting motor parameters, control modes, or initial state. + """ + pass + + @abc.abstractmethod + def get_action(self) -> dict[str, Any]: + """ + Retrieve the current action from the teleoperator. + + Returns: + dict[str, Any]: A flat dictionary representing the teleoperator's current actions. Its + structure should match :pymeth:`observation_features`. + """ + pass + + @abc.abstractmethod + def send_feedback(self, feedback: dict[str, Any]) -> None: + """ + Send a feedback action command to the teleoperator. + + Args: + feedback (dict[str, Any]): Dictionary representing the desired feedback. Its structure should match + :pymeth:`feedback_features`. + + Returns: + dict[str, Any]: The action actually sent to the motors potentially clipped or modified, e.g. by + safety limits on velocity. + """ + pass + + @abc.abstractmethod + def disconnect(self) -> None: + """Disconnect from the teleoperator and perform any necessary cleanup.""" + pass diff --git a/lerobot/common/teleoperators/utils.py b/lerobot/common/teleoperators/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3166793579285b6d3accd24bbb02ab41de94c0f7 --- /dev/null +++ b/lerobot/common/teleoperators/utils.py @@ -0,0 +1,53 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config import TeleoperatorConfig +from .teleoperator import Teleoperator + + +def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator: + if config.type == "keyboard": + from .keyboard import KeyboardTeleop + + return KeyboardTeleop(config) + elif config.type == "koch_leader": + from .koch_leader import KochLeader + + return KochLeader(config) + elif config.type == "so100_leader": + from .so100_leader import SO100Leader + + return SO100Leader(config) + elif config.type == "so101_leader": + from .so101_leader import SO101Leader + + return SO101Leader(config) + elif config.type == "stretch3": + from .stretch3_gamepad import Stretch3GamePad + + return Stretch3GamePad(config) + elif config.type == "widowx": + from .widowx import WidowX + + return WidowX(config) + elif config.type == "mock_teleop": + from tests.mocks.mock_teleop import MockTeleop + + return MockTeleop(config) + elif config.type == "gamepad": + from .gamepad.teleop_gamepad import GamepadTeleop + + return GamepadTeleop(config) + else: + raise ValueError(config.type) diff --git a/lerobot/common/teleoperators/widowx/__init__.py b/lerobot/common/teleoperators/widowx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..09c0d4f0c065f29e5d4e4c9ba5ce8cb92f65abfe --- /dev/null +++ b/lerobot/common/teleoperators/widowx/__init__.py @@ -0,0 +1,2 @@ +from .config_widowx import WidowXConfig +from .widowx import WidowX diff --git a/lerobot/common/teleoperators/widowx/config_widowx.py b/lerobot/common/teleoperators/widowx/config_widowx.py new file mode 100644 index 0000000000000000000000000000000000000000..768b813c8f195eecb11e877039b2e734ae585ef2 --- /dev/null +++ b/lerobot/common/teleoperators/widowx/config_widowx.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("widowx") +@dataclass +class WidowXConfig(TeleoperatorConfig): + port: str # Port to connect to the arm diff --git a/lerobot/common/teleoperators/widowx/widowx.py b/lerobot/common/teleoperators/widowx/widowx.py new file mode 100644 index 0000000000000000000000000000000000000000..8d31f5e37c5f3f2a559a39a4271d36fcf764d959 --- /dev/null +++ b/lerobot/common/teleoperators/widowx/widowx.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.common.motors.dynamixel import ( + DriveMode, + DynamixelMotorsBus, + OperatingMode, +) + +from ..teleoperator import Teleoperator +from .config_widowx import WidowXConfig + +logger = logging.getLogger(__name__) + + +class WidowX(Teleoperator): + """ + [WidowX](https://www.trossenrobotics.com/widowx-250) developed by Trossen Robotics + """ + + config_class = WidowXConfig + name = "widowx" + + def __init__(self, config: WidowXConfig): + raise NotImplementedError + super().__init__(config) + self.config = config + self.bus = DynamixelMotorsBus( + port=self.config.port, + motors={ + "waist": Motor(1, "xm430-w350", MotorNormMode.RANGE_M100_100), + "shoulder": Motor(2, "xm430-w350", MotorNormMode.RANGE_M100_100), + "shoulder_shadow": Motor(3, "xm430-w350", MotorNormMode.RANGE_M100_100), + "elbow": Motor(4, "xm430-w350", MotorNormMode.RANGE_M100_100), + "elbow_shadow": Motor(5, "xm430-w350", MotorNormMode.RANGE_M100_100), + "forearm_roll": Motor(6, "xm430-w350", MotorNormMode.RANGE_M100_100), + "wrist_angle": Motor(7, "xm430-w350", MotorNormMode.RANGE_M100_100), + "wrist_rotate": Motor(8, "xl430-w250", MotorNormMode.RANGE_M100_100), + "gripper": Motor(9, "xc430-w150", MotorNormMode.RANGE_0_100), + }, + ) + + @property + def action_features(self) -> dict[str, type]: + return {f"{motor}.pos": float for motor in self.bus.motors} + + @property + def feedback_features(self) -> dict[str, type]: + return {} + + @property + def is_connected(self) -> bool: + return self.bus.is_connected + + def connect(self, calibrate: bool = True): + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.bus.connect() + if not self.is_calibrated and calibrate: + self.calibrate() + + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + return self.bus.is_calibrated + + def calibrate(self) -> None: + raise NotImplementedError # TODO(aliberts): adapt code below (copied from koch) + logger.info(f"\nRunning calibration of {self}") + self.bus.disable_torque() + for motor in self.bus.motors: + self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value) + + self.bus.write("Drive_Mode", "elbow_flex", DriveMode.INVERTED.value) + drive_modes = {motor: 1 if motor == "elbow_flex" else 0 for motor in self.bus.motors} + + input("Move robot to the middle of its range of motion and press ENTER....") + homing_offsets = self.bus.set_half_turn_homings() + + full_turn_motors = ["shoulder_pan", "wrist_roll"] + unknown_range_motors = [motor for motor in self.bus.motors if motor not in full_turn_motors] + print( + f"Move all joints except {full_turn_motors} sequentially through their " + "entire ranges of motion.\nRecording positions. Press ENTER to stop..." + ) + range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors) + for motor in full_turn_motors: + range_mins[motor] = 0 + range_maxes[motor] = 4095 + + self.calibration = {} + for motor, m in self.bus.motors.items(): + self.calibration[motor] = MotorCalibration( + id=m.id, + drive_mode=drive_modes[motor], + homing_offset=homing_offsets[motor], + range_min=range_mins[motor], + range_max=range_maxes[motor], + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + logger.info(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + self.bus.disable_torque() + self.bus.configure_motors() + + # Set secondary/shadow ID for shoulder and elbow. These joints have two motors. + # As a result, if only one of them is required to move to a certain position, + # the other will follow. This is to avoid breaking the motors. + self.bus.write("Secondary_ID", "shoulder_shadow", 2) + self.bus.write("Secondary_ID", "elbow_shadow", 4) + + def get_action(self) -> dict[str, float]: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + start = time.perf_counter() + action = self.bus.sync_read("Present_Position") + action = {f"{motor}.pos": val for motor, val in action.items()} + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read action: {dt_ms:.1f}ms") + return action + + def send_feedback(self, feedback: dict[str, float]) -> None: + raise NotImplementedError + + def disconnect(self) -> None: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self.bus.disconnect() + logger.info(f"{self} disconnected.") diff --git a/lerobot/common/transport/services.proto b/lerobot/common/transport/services.proto new file mode 100644 index 0000000000000000000000000000000000000000..ba91aab90c860fc24eb5d155232437ea8babe52e --- /dev/null +++ b/lerobot/common/transport/services.proto @@ -0,0 +1,59 @@ +// Copyright 2024 The HuggingFace Inc. team. +// All rights reserved. + +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at + +// http://www.apache.org/licenses/LICENSE-2.0 + +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// To generate a classes for transport part (services_pb2.py and services_pb2_grpc.py) use the following command: +// +// python -m grpc_tools.protoc -I . --python_out=. --grpc_python_out=. lerobot/common/transport/services.proto +// +// The command should be launched from the root of the project. + +syntax = "proto3"; + +package transport; + +// LearnerService: the Actor calls this to push transitions. +// The Learner implements this service. +service LearnerService { + // Actor -> Learner to store transitions + rpc StreamParameters(Empty) returns (stream Parameters); + rpc SendTransitions(stream Transition) returns (Empty); + rpc SendInteractions(stream InteractionMessage) returns (Empty); + rpc Ready(Empty) returns (Empty); +} + +enum TransferState { + TRANSFER_UNKNOWN = 0; + TRANSFER_BEGIN = 1; + TRANSFER_MIDDLE = 2; + TRANSFER_END = 3; +} + +// Messages +message Transition { + TransferState transfer_state = 1; + bytes data = 2; +} + +message Parameters { + TransferState transfer_state = 1; + bytes data = 2; +} + +message InteractionMessage { + TransferState transfer_state = 1; + bytes data = 2; +} + +message Empty {} diff --git a/lerobot/common/transport/services_pb2.py b/lerobot/common/transport/services_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..ab31a1496a366d3a203dd978a486d48742717046 --- /dev/null +++ b/lerobot/common/transport/services_pb2.py @@ -0,0 +1,45 @@ +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: lerobot/common/transport/services.proto +# Protobuf Python Version: 5.29.0 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 29, + 0, + '', + 'lerobot/common/transport/services.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'lerobot/common/transport/services.proto\x12\ttransport\"L\n\nTransition\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"L\n\nParameters\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"T\n\x12InteractionMessage\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"\x07\n\x05\x45mpty*`\n\rTransferState\x12\x14\n\x10TRANSFER_UNKNOWN\x10\x00\x12\x12\n\x0eTRANSFER_BEGIN\x10\x01\x12\x13\n\x0fTRANSFER_MIDDLE\x10\x02\x12\x10\n\x0cTRANSFER_END\x10\x03\x32\x81\x02\n\x0eLearnerService\x12=\n\x10StreamParameters\x12\x10.transport.Empty\x1a\x15.transport.Parameters0\x01\x12<\n\x0fSendTransitions\x12\x15.transport.Transition\x1a\x10.transport.Empty(\x01\x12\x45\n\x10SendInteractions\x12\x1d.transport.InteractionMessage\x1a\x10.transport.Empty(\x01\x12+\n\x05Ready\x12\x10.transport.Empty\x1a\x10.transport.Emptyb\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'lerobot.common.transport.services_pb2', _globals) +if not _descriptor._USE_C_DESCRIPTORS: + DESCRIPTOR._loaded_options = None + _globals['_TRANSFERSTATE']._serialized_start=305 + _globals['_TRANSFERSTATE']._serialized_end=401 + _globals['_TRANSITION']._serialized_start=54 + _globals['_TRANSITION']._serialized_end=130 + _globals['_PARAMETERS']._serialized_start=132 + _globals['_PARAMETERS']._serialized_end=208 + _globals['_INTERACTIONMESSAGE']._serialized_start=210 + _globals['_INTERACTIONMESSAGE']._serialized_end=294 + _globals['_EMPTY']._serialized_start=296 + _globals['_EMPTY']._serialized_end=303 + _globals['_LEARNERSERVICE']._serialized_start=404 + _globals['_LEARNERSERVICE']._serialized_end=661 +# @@protoc_insertion_point(module_scope) diff --git a/lerobot/common/transport/services_pb2_grpc.py b/lerobot/common/transport/services_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..cdc7db085e40d8efb77ce204d4b2354d0679ecc9 --- /dev/null +++ b/lerobot/common/transport/services_pb2_grpc.py @@ -0,0 +1,233 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + +from lerobot.common.transport import services_pb2 as lerobot_dot_common_dot_transport_dot_services__pb2 + +GRPC_GENERATED_VERSION = '1.71.0' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in lerobot/common/transport/services_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) + + +class LearnerServiceStub: + """LearnerService: the Actor calls this to push transitions. + The Learner implements this service. + """ + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.StreamParameters = channel.unary_stream( + '/transport.LearnerService/StreamParameters', + request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Parameters.FromString, + _registered_method=True) + self.SendTransitions = channel.stream_unary( + '/transport.LearnerService/SendTransitions', + request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Transition.SerializeToString, + response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + _registered_method=True) + self.SendInteractions = channel.stream_unary( + '/transport.LearnerService/SendInteractions', + request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.SerializeToString, + response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + _registered_method=True) + self.Ready = channel.unary_unary( + '/transport.LearnerService/Ready', + request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + _registered_method=True) + + +class LearnerServiceServicer: + """LearnerService: the Actor calls this to push transitions. + The Learner implements this service. + """ + + def StreamParameters(self, request, context): + """Actor -> Learner to store transitions + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SendTransitions(self, request_iterator, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SendInteractions(self, request_iterator, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def Ready(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_LearnerServiceServicer_to_server(servicer, server): + rpc_method_handlers = { + 'StreamParameters': grpc.unary_stream_rpc_method_handler( + servicer.StreamParameters, + request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Parameters.SerializeToString, + ), + 'SendTransitions': grpc.stream_unary_rpc_method_handler( + servicer.SendTransitions, + request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Transition.FromString, + response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + ), + 'SendInteractions': grpc.stream_unary_rpc_method_handler( + servicer.SendInteractions, + request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.FromString, + response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + ), + 'Ready': grpc.unary_unary_rpc_method_handler( + servicer.Ready, + request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'transport.LearnerService', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + server.add_registered_method_handlers('transport.LearnerService', rpc_method_handlers) + + + # This class is part of an EXPERIMENTAL API. +class LearnerService: + """LearnerService: the Actor calls this to push transitions. + The Learner implements this service. + """ + + @staticmethod + def StreamParameters(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_stream( + request, + target, + '/transport.LearnerService/StreamParameters', + lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + lerobot_dot_common_dot_transport_dot_services__pb2.Parameters.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SendTransitions(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_unary( + request_iterator, + target, + '/transport.LearnerService/SendTransitions', + lerobot_dot_common_dot_transport_dot_services__pb2.Transition.SerializeToString, + lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SendInteractions(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_unary( + request_iterator, + target, + '/transport.LearnerService/SendInteractions', + lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.SerializeToString, + lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def Ready(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/transport.LearnerService/Ready', + lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString, + lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) diff --git a/lerobot/common/transport/utils.py b/lerobot/common/transport/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bb430c948b13032bfad76299b03855477bd1b17e --- /dev/null +++ b/lerobot/common/transport/utils.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import io +import logging +import pickle # nosec B403: Safe usage for internal serialization only +from multiprocessing import Event, Queue +from typing import Any + +import torch + +from lerobot.common.transport import services_pb2 +from lerobot.common.utils.transition import Transition + +CHUNK_SIZE = 2 * 1024 * 1024 # 2 MB + + +def bytes_buffer_size(buffer: io.BytesIO) -> int: + buffer.seek(0, io.SEEK_END) + result = buffer.tell() + buffer.seek(0) + return result + + +def send_bytes_in_chunks(buffer: bytes, message_class: Any, log_prefix: str = "", silent: bool = True): + buffer = io.BytesIO(buffer) + size_in_bytes = bytes_buffer_size(buffer) + + sent_bytes = 0 + + logging_method = logging.info if not silent else logging.debug + + logging_method(f"{log_prefix} Buffer size {size_in_bytes / 1024 / 1024} MB with") + + while sent_bytes < size_in_bytes: + transfer_state = services_pb2.TransferState.TRANSFER_MIDDLE + + if sent_bytes + CHUNK_SIZE >= size_in_bytes: + transfer_state = services_pb2.TransferState.TRANSFER_END + elif sent_bytes == 0: + transfer_state = services_pb2.TransferState.TRANSFER_BEGIN + + size_to_read = min(CHUNK_SIZE, size_in_bytes - sent_bytes) + chunk = buffer.read(size_to_read) + + yield message_class(transfer_state=transfer_state, data=chunk) + sent_bytes += size_to_read + logging_method(f"{log_prefix} Sent {sent_bytes}/{size_in_bytes} bytes with state {transfer_state}") + + logging_method(f"{log_prefix} Published {sent_bytes / 1024 / 1024} MB") + + +def receive_bytes_in_chunks(iterator, queue: Queue, shutdown_event: Event, log_prefix: str = ""): # type: ignore + bytes_buffer = io.BytesIO() + step = 0 + + logging.info(f"{log_prefix} Starting receiver") + for item in iterator: + logging.debug(f"{log_prefix} Received item") + if shutdown_event.is_set(): + logging.info(f"{log_prefix} Shutting down receiver") + return + + if item.transfer_state == services_pb2.TransferState.TRANSFER_BEGIN: + bytes_buffer.seek(0) + bytes_buffer.truncate(0) + bytes_buffer.write(item.data) + logging.debug(f"{log_prefix} Received data at step 0") + step = 0 + elif item.transfer_state == services_pb2.TransferState.TRANSFER_MIDDLE: + bytes_buffer.write(item.data) + step += 1 + logging.debug(f"{log_prefix} Received data at step {step}") + elif item.transfer_state == services_pb2.TransferState.TRANSFER_END: + bytes_buffer.write(item.data) + logging.debug(f"{log_prefix} Received data at step end size {bytes_buffer_size(bytes_buffer)}") + + queue.put(bytes_buffer.getvalue()) + + bytes_buffer.seek(0) + bytes_buffer.truncate(0) + step = 0 + + logging.debug(f"{log_prefix} Queue updated") + else: + logging.warning(f"{log_prefix} Received unknown transfer state {item.transfer_state}") + raise ValueError(f"Received unknown transfer state {item.transfer_state}") + + +def state_to_bytes(state_dict: dict[str, torch.Tensor]) -> bytes: + """Convert model state dict to flat array for transmission""" + buffer = io.BytesIO() + + torch.save(state_dict, buffer) + + return buffer.getvalue() + + +def bytes_to_state_dict(buffer: bytes) -> dict[str, torch.Tensor]: + buffer = io.BytesIO(buffer) + buffer.seek(0) + return torch.load(buffer, weights_only=True) + + +def python_object_to_bytes(python_object: Any) -> bytes: + return pickle.dumps(python_object) + + +def bytes_to_python_object(buffer: bytes) -> Any: + buffer = io.BytesIO(buffer) + buffer.seek(0) + obj = pickle.load(buffer) # nosec B301: Safe usage of pickle.load + # Add validation checks here + return obj + + +def bytes_to_transitions(buffer: bytes) -> list[Transition]: + buffer = io.BytesIO(buffer) + buffer.seek(0) + transitions = torch.load(buffer, weights_only=True) + return transitions + + +def transitions_to_bytes(transitions: list[Transition]) -> bytes: + buffer = io.BytesIO() + torch.save(transitions, buffer) + return buffer.getvalue() diff --git a/lerobot/common/utils/benchmark.py b/lerobot/common/utils/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..3c1aedbc7a92b6c603f9c05cb6e71567253f038b --- /dev/null +++ b/lerobot/common/utils/benchmark.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import threading +import time +from contextlib import ContextDecorator + + +class TimeBenchmark(ContextDecorator): + """ + Measures execution time using a context manager or decorator. + + This class supports both context manager and decorator usage, and is thread-safe for multithreaded + environments. + + Args: + print: If True, prints the elapsed time upon exiting the context or completing the function. Defaults + to False. + + Examples: + + Using as a context manager: + + >>> benchmark = TimeBenchmark() + >>> with benchmark: + ... time.sleep(1) + >>> print(f"Block took {benchmark.result:.4f} seconds") + Block took approximately 1.0000 seconds + + Using with multithreading: + + ```python + import threading + + benchmark = TimeBenchmark() + + def context_manager_example(): + with benchmark: + time.sleep(0.01) + print(f"Block took {benchmark.result_ms:.2f} milliseconds") + + threads = [] + for _ in range(3): + t1 = threading.Thread(target=context_manager_example) + threads.append(t1) + + for t in threads: + t.start() + + for t in threads: + t.join() + ``` + Expected output: + Block took approximately 10.00 milliseconds + Block took approximately 10.00 milliseconds + Block took approximately 10.00 milliseconds + """ + + def __init__(self, print=False): + self.local = threading.local() + self.print_time = print + + def __enter__(self): + self.local.start_time = time.perf_counter() + return self + + def __exit__(self, *exc): + self.local.end_time = time.perf_counter() + self.local.elapsed_time = self.local.end_time - self.local.start_time + if self.print_time: + print(f"Elapsed time: {self.local.elapsed_time:.4f} seconds") + return False + + @property + def result(self): + return getattr(self.local, "elapsed_time", None) + + @property + def result_ms(self): + return self.result * 1e3 diff --git a/lerobot/common/utils/buffer.py b/lerobot/common/utils/buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..57af90a06a8bd7f9f777a86c3dfc12ed66ce1770 --- /dev/null +++ b/lerobot/common/utils/buffer.py @@ -0,0 +1,841 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import functools +from contextlib import suppress +from typing import Callable, Sequence, TypedDict + +import torch +import torch.nn.functional as F # noqa: N812 +from tqdm import tqdm + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.utils.transition import Transition + + +class BatchTransition(TypedDict): + state: dict[str, torch.Tensor] + action: torch.Tensor + reward: torch.Tensor + next_state: dict[str, torch.Tensor] + done: torch.Tensor + truncated: torch.Tensor + complementary_info: dict[str, torch.Tensor | float | int] | None = None + + +def random_crop_vectorized(images: torch.Tensor, output_size: tuple) -> torch.Tensor: + """ + Perform a per-image random crop over a batch of images in a vectorized way. + (Same as shown previously.) + """ + B, C, H, W = images.shape # noqa: N806 + crop_h, crop_w = output_size + + if crop_h > H or crop_w > W: + raise ValueError( + f"Requested crop size ({crop_h}, {crop_w}) is bigger than the image size ({H}, {W})." + ) + + tops = torch.randint(0, H - crop_h + 1, (B,), device=images.device) + lefts = torch.randint(0, W - crop_w + 1, (B,), device=images.device) + + rows = torch.arange(crop_h, device=images.device).unsqueeze(0) + tops.unsqueeze(1) + cols = torch.arange(crop_w, device=images.device).unsqueeze(0) + lefts.unsqueeze(1) + + rows = rows.unsqueeze(2).expand(-1, -1, crop_w) # (B, crop_h, crop_w) + cols = cols.unsqueeze(1).expand(-1, crop_h, -1) # (B, crop_h, crop_w) + + images_hwcn = images.permute(0, 2, 3, 1) # (B, H, W, C) + + # Gather pixels + cropped_hwcn = images_hwcn[torch.arange(B, device=images.device).view(B, 1, 1), rows, cols, :] + # cropped_hwcn => (B, crop_h, crop_w, C) + + cropped = cropped_hwcn.permute(0, 3, 1, 2) # (B, C, crop_h, crop_w) + return cropped + + +def random_shift(images: torch.Tensor, pad: int = 4): + """Vectorized random shift, imgs: (B,C,H,W), pad: #pixels""" + _, _, h, w = images.shape + images = F.pad(input=images, pad=(pad, pad, pad, pad), mode="replicate") + return random_crop_vectorized(images=images, output_size=(h, w)) + + +class ReplayBuffer: + def __init__( + self, + capacity: int, + device: str = "cuda:0", + state_keys: Sequence[str] | None = None, + image_augmentation_function: Callable | None = None, + use_drq: bool = True, + storage_device: str = "cpu", + optimize_memory: bool = False, + ): + """ + Replay buffer for storing transitions. + It will allocate tensors on the specified device, when the first transition is added. + NOTE: If you encounter memory issues, you can try to use the `optimize_memory` flag to save memory or + and use the `storage_device` flag to store the buffer on a different device. + Args: + capacity (int): Maximum number of transitions to store in the buffer. + device (str): The device where the tensors will be moved when sampling ("cuda:0" or "cpu"). + state_keys (List[str]): The list of keys that appear in `state` and `next_state`. + image_augmentation_function (Optional[Callable]): A function that takes a batch of images + and returns a batch of augmented images. If None, a default augmentation function is used. + use_drq (bool): Whether to use the default DRQ image augmentation style, when sampling in the buffer. + storage_device: The device (e.g. "cpu" or "cuda:0") where the data will be stored. + Using "cpu" can help save GPU memory. + optimize_memory (bool): If True, optimizes memory by not storing duplicate next_states when + they can be derived from states. This is useful for large datasets where next_state[i] = state[i+1]. + """ + if capacity <= 0: + raise ValueError("Capacity must be greater than 0.") + + self.capacity = capacity + self.device = device + self.storage_device = storage_device + self.position = 0 + self.size = 0 + self.initialized = False + self.optimize_memory = optimize_memory + + # Track episode boundaries for memory optimization + self.episode_ends = torch.zeros(capacity, dtype=torch.bool, device=storage_device) + + # If no state_keys provided, default to an empty list + self.state_keys = state_keys if state_keys is not None else [] + + self.image_augmentation_function = image_augmentation_function + + if image_augmentation_function is None: + base_function = functools.partial(random_shift, pad=4) + self.image_augmentation_function = torch.compile(base_function) + self.use_drq = use_drq + + def _initialize_storage( + self, + state: dict[str, torch.Tensor], + action: torch.Tensor, + complementary_info: dict[str, torch.Tensor] | None = None, + ): + """Initialize the storage tensors based on the first transition.""" + # Determine shapes from the first transition + state_shapes = {key: val.squeeze(0).shape for key, val in state.items()} + action_shape = action.squeeze(0).shape + + # Pre-allocate tensors for storage + self.states = { + key: torch.empty((self.capacity, *shape), device=self.storage_device) + for key, shape in state_shapes.items() + } + self.actions = torch.empty((self.capacity, *action_shape), device=self.storage_device) + self.rewards = torch.empty((self.capacity,), device=self.storage_device) + + if not self.optimize_memory: + # Standard approach: store states and next_states separately + self.next_states = { + key: torch.empty((self.capacity, *shape), device=self.storage_device) + for key, shape in state_shapes.items() + } + else: + # Memory-optimized approach: don't allocate next_states buffer + # Just create a reference to states for consistent API + self.next_states = self.states # Just a reference for API consistency + + self.dones = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device) + self.truncateds = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device) + + # Initialize storage for complementary_info + self.has_complementary_info = complementary_info is not None + self.complementary_info_keys = [] + self.complementary_info = {} + + if self.has_complementary_info: + self.complementary_info_keys = list(complementary_info.keys()) + # Pre-allocate tensors for each key in complementary_info + for key, value in complementary_info.items(): + if isinstance(value, torch.Tensor): + value_shape = value.squeeze(0).shape + self.complementary_info[key] = torch.empty( + (self.capacity, *value_shape), device=self.storage_device + ) + elif isinstance(value, (int, float)): + # Handle scalar values similar to reward + self.complementary_info[key] = torch.empty((self.capacity,), device=self.storage_device) + else: + raise ValueError(f"Unsupported type {type(value)} for complementary_info[{key}]") + + self.initialized = True + + def __len__(self): + return self.size + + def add( + self, + state: dict[str, torch.Tensor], + action: torch.Tensor, + reward: float, + next_state: dict[str, torch.Tensor], + done: bool, + truncated: bool, + complementary_info: dict[str, torch.Tensor] | None = None, + ): + """Saves a transition, ensuring tensors are stored on the designated storage device.""" + # Initialize storage if this is the first transition + if not self.initialized: + self._initialize_storage(state=state, action=action, complementary_info=complementary_info) + + # Store the transition in pre-allocated tensors + for key in self.states: + self.states[key][self.position].copy_(state[key].squeeze(dim=0)) + + if not self.optimize_memory: + # Only store next_states if not optimizing memory + self.next_states[key][self.position].copy_(next_state[key].squeeze(dim=0)) + + self.actions[self.position].copy_(action.squeeze(dim=0)) + self.rewards[self.position] = reward + self.dones[self.position] = done + self.truncateds[self.position] = truncated + + # Handle complementary_info if provided and storage is initialized + if complementary_info is not None and self.has_complementary_info: + # Store the complementary_info + for key in self.complementary_info_keys: + if key in complementary_info: + value = complementary_info[key] + if isinstance(value, torch.Tensor): + self.complementary_info[key][self.position].copy_(value.squeeze(dim=0)) + elif isinstance(value, (int, float)): + self.complementary_info[key][self.position] = value + + self.position = (self.position + 1) % self.capacity + self.size = min(self.size + 1, self.capacity) + + def sample(self, batch_size: int) -> BatchTransition: + """Sample a random batch of transitions and collate them into batched tensors.""" + if not self.initialized: + raise RuntimeError("Cannot sample from an empty buffer. Add transitions first.") + + batch_size = min(batch_size, self.size) + high = max(0, self.size - 1) if self.optimize_memory and self.size < self.capacity else self.size + + # Random indices for sampling - create on the same device as storage + idx = torch.randint(low=0, high=high, size=(batch_size,), device=self.storage_device) + + # Identify image keys that need augmentation + image_keys = [k for k in self.states if k.startswith("observation.image")] if self.use_drq else [] + + # Create batched state and next_state + batch_state = {} + batch_next_state = {} + + # First pass: load all state tensors to target device + for key in self.states: + batch_state[key] = self.states[key][idx].to(self.device) + + if not self.optimize_memory: + # Standard approach - load next_states directly + batch_next_state[key] = self.next_states[key][idx].to(self.device) + else: + # Memory-optimized approach - get next_state from the next index + next_idx = (idx + 1) % self.capacity + batch_next_state[key] = self.states[key][next_idx].to(self.device) + + # Apply image augmentation in a batched way if needed + if self.use_drq and image_keys: + # Concatenate all images from state and next_state + all_images = [] + for key in image_keys: + all_images.append(batch_state[key]) + all_images.append(batch_next_state[key]) + + # Optimization: Batch all images and apply augmentation once + all_images_tensor = torch.cat(all_images, dim=0) + augmented_images = self.image_augmentation_function(all_images_tensor) + + # Split the augmented images back to their sources + for i, key in enumerate(image_keys): + # Calculate offsets for the current image key: + # For each key, we have 2*batch_size images (batch_size for states, batch_size for next_states) + # States start at index i*2*batch_size and take up batch_size slots + batch_state[key] = augmented_images[i * 2 * batch_size : (i * 2 + 1) * batch_size] + # Next states start after the states at index (i*2+1)*batch_size and also take up batch_size slots + batch_next_state[key] = augmented_images[(i * 2 + 1) * batch_size : (i + 1) * 2 * batch_size] + + # Sample other tensors + batch_actions = self.actions[idx].to(self.device) + batch_rewards = self.rewards[idx].to(self.device) + batch_dones = self.dones[idx].to(self.device).float() + batch_truncateds = self.truncateds[idx].to(self.device).float() + + # Sample complementary_info if available + batch_complementary_info = None + if self.has_complementary_info: + batch_complementary_info = {} + for key in self.complementary_info_keys: + batch_complementary_info[key] = self.complementary_info[key][idx].to(self.device) + + return BatchTransition( + state=batch_state, + action=batch_actions, + reward=batch_rewards, + next_state=batch_next_state, + done=batch_dones, + truncated=batch_truncateds, + complementary_info=batch_complementary_info, + ) + + def get_iterator( + self, + batch_size: int, + async_prefetch: bool = True, + queue_size: int = 2, + ): + """ + Creates an infinite iterator that yields batches of transitions. + Will automatically restart when internal iterator is exhausted. + + Args: + batch_size (int): Size of batches to sample + async_prefetch (bool): Whether to use asynchronous prefetching with threads (default: True) + queue_size (int): Number of batches to prefetch (default: 2) + + Yields: + BatchTransition: Batched transitions + """ + while True: # Create an infinite loop + if async_prefetch: + # Get the standard iterator + iterator = self._get_async_iterator(queue_size=queue_size, batch_size=batch_size) + else: + iterator = self._get_naive_iterator(batch_size=batch_size, queue_size=queue_size) + + # Yield all items from the iterator + with suppress(StopIteration): + yield from iterator + + def _get_async_iterator(self, batch_size: int, queue_size: int = 2): + """ + Create an iterator that continuously yields prefetched batches in a + background thread. The design is intentionally simple and avoids busy + waiting / complex state management. + + Args: + batch_size (int): Size of batches to sample. + queue_size (int): Maximum number of prefetched batches to keep in + memory. + + Yields: + BatchTransition: A batch sampled from the replay buffer. + """ + import queue + import threading + + data_queue: queue.Queue = queue.Queue(maxsize=queue_size) + shutdown_event = threading.Event() + + def producer() -> None: + """Continuously put sampled batches into the queue until shutdown.""" + while not shutdown_event.is_set(): + try: + batch = self.sample(batch_size) + # The timeout ensures the thread unblocks if the queue is full + # and the shutdown event gets set meanwhile. + data_queue.put(batch, block=True, timeout=0.5) + except queue.Full: + # Queue is full – loop again (will re-check shutdown_event) + continue + except Exception: + # Surface any unexpected error and terminate the producer. + shutdown_event.set() + + producer_thread = threading.Thread(target=producer, daemon=True) + producer_thread.start() + + try: + while not shutdown_event.is_set(): + try: + yield data_queue.get(block=True) + except Exception: + # If the producer already set the shutdown flag we exit. + if shutdown_event.is_set(): + break + finally: + shutdown_event.set() + # Drain the queue quickly to help the thread exit if it's blocked on `put`. + while not data_queue.empty(): + _ = data_queue.get_nowait() + # Give the producer thread a bit of time to finish. + producer_thread.join(timeout=1.0) + + def _get_naive_iterator(self, batch_size: int, queue_size: int = 2): + """ + Creates a simple non-threaded iterator that yields batches. + + Args: + batch_size (int): Size of batches to sample + queue_size (int): Number of initial batches to prefetch + + Yields: + BatchTransition: Batch transitions + """ + import collections + + queue = collections.deque() + + def enqueue(n): + for _ in range(n): + data = self.sample(batch_size) + queue.append(data) + + enqueue(queue_size) + while queue: + yield queue.popleft() + enqueue(1) + + @classmethod + def from_lerobot_dataset( + cls, + lerobot_dataset: LeRobotDataset, + device: str = "cuda:0", + state_keys: Sequence[str] | None = None, + capacity: int | None = None, + image_augmentation_function: Callable | None = None, + use_drq: bool = True, + storage_device: str = "cpu", + optimize_memory: bool = False, + ) -> "ReplayBuffer": + """ + Convert a LeRobotDataset into a ReplayBuffer. + + Args: + lerobot_dataset (LeRobotDataset): The dataset to convert. + device (str): The device for sampling tensors. Defaults to "cuda:0". + state_keys (Sequence[str] | None): The list of keys that appear in `state` and `next_state`. + capacity (int | None): Buffer capacity. If None, uses dataset length. + action_mask (Sequence[int] | None): Indices of action dimensions to keep. + image_augmentation_function (Callable | None): Function for image augmentation. + If None, uses default random shift with pad=4. + use_drq (bool): Whether to use DrQ image augmentation when sampling. + storage_device (str): Device for storing tensor data. Using "cpu" saves GPU memory. + optimize_memory (bool): If True, reduces memory usage by not duplicating state data. + + Returns: + ReplayBuffer: The replay buffer with dataset transitions. + """ + if capacity is None: + capacity = len(lerobot_dataset) + + if capacity < len(lerobot_dataset): + raise ValueError( + "The capacity of the ReplayBuffer must be greater than or equal to the length of the LeRobotDataset." + ) + + # Create replay buffer with image augmentation and DrQ settings + replay_buffer = cls( + capacity=capacity, + device=device, + state_keys=state_keys, + image_augmentation_function=image_augmentation_function, + use_drq=use_drq, + storage_device=storage_device, + optimize_memory=optimize_memory, + ) + + # Convert dataset to transitions + list_transition = cls._lerobotdataset_to_transitions(dataset=lerobot_dataset, state_keys=state_keys) + + # Initialize the buffer with the first transition to set up storage tensors + if list_transition: + first_transition = list_transition[0] + first_state = {k: v.to(device) for k, v in first_transition["state"].items()} + first_action = first_transition["action"].to(device) + + # Get complementary info if available + first_complementary_info = None + if ( + "complementary_info" in first_transition + and first_transition["complementary_info"] is not None + ): + first_complementary_info = { + k: v.to(device) for k, v in first_transition["complementary_info"].items() + } + + replay_buffer._initialize_storage( + state=first_state, action=first_action, complementary_info=first_complementary_info + ) + + # Fill the buffer with all transitions + for data in list_transition: + for k, v in data.items(): + if isinstance(v, dict): + for key, tensor in v.items(): + v[key] = tensor.to(storage_device) + elif isinstance(v, torch.Tensor): + data[k] = v.to(storage_device) + + action = data["action"] + + replay_buffer.add( + state=data["state"], + action=action, + reward=data["reward"], + next_state=data["next_state"], + done=data["done"], + truncated=False, # NOTE: Truncation are not supported yet in lerobot dataset + complementary_info=data.get("complementary_info", None), + ) + + return replay_buffer + + def to_lerobot_dataset( + self, + repo_id: str, + fps=1, + root=None, + task_name="from_replay_buffer", + ) -> LeRobotDataset: + """ + Converts all transitions in this ReplayBuffer into a single LeRobotDataset object. + """ + if self.size == 0: + raise ValueError("The replay buffer is empty. Cannot convert to a dataset.") + + # Create features dictionary for the dataset + features = { + "index": {"dtype": "int64", "shape": [1]}, # global index across episodes + "episode_index": {"dtype": "int64", "shape": [1]}, # which episode + "frame_index": {"dtype": "int64", "shape": [1]}, # index inside an episode + "timestamp": {"dtype": "float32", "shape": [1]}, # for now we store dummy + "task_index": {"dtype": "int64", "shape": [1]}, + } + + # Add "action" + sample_action = self.actions[0] + act_info = guess_feature_info(t=sample_action, name="action") + features["action"] = act_info + + # Add "reward" and "done" + features["next.reward"] = {"dtype": "float32", "shape": (1,)} + features["next.done"] = {"dtype": "bool", "shape": (1,)} + + # Add state keys + for key in self.states: + sample_val = self.states[key][0] + f_info = guess_feature_info(t=sample_val, name=key) + features[key] = f_info + + # Add complementary_info keys if available + if self.has_complementary_info: + for key in self.complementary_info_keys: + sample_val = self.complementary_info[key][0] + if isinstance(sample_val, torch.Tensor) and sample_val.ndim == 0: + sample_val = sample_val.unsqueeze(0) + f_info = guess_feature_info(t=sample_val, name=f"complementary_info.{key}") + features[f"complementary_info.{key}"] = f_info + + # Create an empty LeRobotDataset + lerobot_dataset = LeRobotDataset.create( + repo_id=repo_id, + fps=fps, + root=root, + robot_type=None, + features=features, + use_videos=True, + ) + + # Start writing images if needed + lerobot_dataset.start_image_writer(num_processes=0, num_threads=3) + + # Convert transitions into episodes and frames + episode_index = 0 + lerobot_dataset.episode_buffer = lerobot_dataset.create_episode_buffer(episode_index=episode_index) + + frame_idx_in_episode = 0 + for idx in range(self.size): + actual_idx = (self.position - self.size + idx) % self.capacity + + frame_dict = {} + + # Fill the data for state keys + for key in self.states: + frame_dict[key] = self.states[key][actual_idx].cpu() + + # Fill action, reward, done + frame_dict["action"] = self.actions[actual_idx].cpu() + frame_dict["next.reward"] = torch.tensor([self.rewards[actual_idx]], dtype=torch.float32).cpu() + frame_dict["next.done"] = torch.tensor([self.dones[actual_idx]], dtype=torch.bool).cpu() + + # Add complementary_info if available + if self.has_complementary_info: + for key in self.complementary_info_keys: + val = self.complementary_info[key][actual_idx] + # Convert tensors to CPU + if isinstance(val, torch.Tensor): + if val.ndim == 0: + val = val.unsqueeze(0) + frame_dict[f"complementary_info.{key}"] = val.cpu() + # Non-tensor values can be used directly + else: + frame_dict[f"complementary_info.{key}"] = val + + # Add to the dataset's buffer + lerobot_dataset.add_frame(frame_dict, task=task_name) + + # Move to next frame + frame_idx_in_episode += 1 + + # If we reached an episode boundary, call save_episode, reset counters + if self.dones[actual_idx] or self.truncateds[actual_idx]: + lerobot_dataset.save_episode() + episode_index += 1 + frame_idx_in_episode = 0 + lerobot_dataset.episode_buffer = lerobot_dataset.create_episode_buffer( + episode_index=episode_index + ) + + # Save any remaining frames in the buffer + if lerobot_dataset.episode_buffer["size"] > 0: + lerobot_dataset.save_episode() + + lerobot_dataset.stop_image_writer() + + return lerobot_dataset + + @staticmethod + def _lerobotdataset_to_transitions( + dataset: LeRobotDataset, + state_keys: Sequence[str] | None = None, + ) -> list[Transition]: + """ + Convert a LeRobotDataset into a list of RL (s, a, r, s', done) transitions. + + Args: + dataset (LeRobotDataset): + The dataset to convert. Each item in the dataset is expected to have + at least the following keys: + { + "action": ... + "next.reward": ... + "next.done": ... + "episode_index": ... + } + plus whatever your 'state_keys' specify. + + state_keys (Sequence[str] | None): + The dataset keys to include in 'state' and 'next_state'. Their names + will be kept as-is in the output transitions. E.g. + ["observation.state", "observation.environment_state"]. + If None, you must handle or define default keys. + + Returns: + transitions (List[Transition]): + A list of Transition dictionaries with the same length as `dataset`. + """ + if state_keys is None: + raise ValueError("State keys must be provided when converting LeRobotDataset to Transitions.") + + transitions = [] + num_frames = len(dataset) + + # Check if the dataset has "next.done" key + sample = dataset[0] + has_done_key = "next.done" in sample + + # Check for complementary_info keys + complementary_info_keys = [key for key in sample if key.startswith("complementary_info.")] + has_complementary_info = len(complementary_info_keys) > 0 + + # If not, we need to infer it from episode boundaries + if not has_done_key: + print("'next.done' key not found in dataset. Inferring from episode boundaries...") + + for i in tqdm(range(num_frames)): + current_sample = dataset[i] + + # ----- 1) Current state ----- + current_state: dict[str, torch.Tensor] = {} + for key in state_keys: + val = current_sample[key] + current_state[key] = val.unsqueeze(0) # Add batch dimension + + # ----- 2) Action ----- + action = current_sample["action"].unsqueeze(0) # Add batch dimension + + # ----- 3) Reward and done ----- + reward = float(current_sample["next.reward"].item()) # ensure float + + # Determine done flag - use next.done if available, otherwise infer from episode boundaries + if has_done_key: + done = bool(current_sample["next.done"].item()) # ensure bool + else: + # If this is the last frame or if next frame is in a different episode, mark as done + done = False + if i == num_frames - 1: + done = True + elif i < num_frames - 1: + next_sample = dataset[i + 1] + if next_sample["episode_index"] != current_sample["episode_index"]: + done = True + + # TODO: (azouitine) Handle truncation (using the same value as done for now) + truncated = done + + # ----- 4) Next state ----- + # If not done and the next sample is in the same episode, we pull the next sample's state. + # Otherwise (done=True or next sample crosses to a new episode), next_state = current_state. + next_state = current_state # default + if not done and (i < num_frames - 1): + next_sample = dataset[i + 1] + if next_sample["episode_index"] == current_sample["episode_index"]: + # Build next_state from the same keys + next_state_data: dict[str, torch.Tensor] = {} + for key in state_keys: + val = next_sample[key] + next_state_data[key] = val.unsqueeze(0) # Add batch dimension + next_state = next_state_data + + # ----- 5) Complementary info (if available) ----- + complementary_info = None + if has_complementary_info: + complementary_info = {} + for key in complementary_info_keys: + # Strip the "complementary_info." prefix to get the actual key + clean_key = key[len("complementary_info.") :] + val = current_sample[key] + # Handle tensor and non-tensor values differently + if isinstance(val, torch.Tensor): + complementary_info[clean_key] = val.unsqueeze(0) # Add batch dimension + else: + # TODO: (azouitine) Check if it's necessary to convert to tensor + # For non-tensor values, use directly + complementary_info[clean_key] = val + + # ----- Construct the Transition ----- + transition = Transition( + state=current_state, + action=action, + reward=reward, + next_state=next_state, + done=done, + truncated=truncated, + complementary_info=complementary_info, + ) + transitions.append(transition) + + return transitions + + +# Utility function to guess shapes/dtypes from a tensor +def guess_feature_info(t, name: str): + """ + Return a dictionary with the 'dtype' and 'shape' for a given tensor or scalar value. + If it looks like a 3D (C,H,W) shape, we might consider it an 'image'. + Otherwise default to appropriate dtype for numeric. + """ + + shape = tuple(t.shape) + # Basic guess: if we have exactly 3 dims and shape[0] in {1, 3}, guess 'image' + if len(shape) == 3 and shape[0] in [1, 3]: + return { + "dtype": "image", + "shape": shape, + } + else: + # Otherwise treat as numeric + return { + "dtype": "float32", + "shape": shape, + } + + +def concatenate_batch_transitions( + left_batch_transitions: BatchTransition, right_batch_transition: BatchTransition +) -> BatchTransition: + """ + Concatenates two BatchTransition objects into one. + + This function merges the right BatchTransition into the left one by concatenating + all corresponding tensors along dimension 0. The operation modifies the left_batch_transitions + in place and also returns it. + + Args: + left_batch_transitions (BatchTransition): The first batch to concatenate and the one + that will be modified in place. + right_batch_transition (BatchTransition): The second batch to append to the first one. + + Returns: + BatchTransition: The concatenated batch (same object as left_batch_transitions). + + Warning: + This function modifies the left_batch_transitions object in place. + """ + # Concatenate state fields + left_batch_transitions["state"] = { + key: torch.cat( + [left_batch_transitions["state"][key], right_batch_transition["state"][key]], + dim=0, + ) + for key in left_batch_transitions["state"] + } + + # Concatenate basic fields + left_batch_transitions["action"] = torch.cat( + [left_batch_transitions["action"], right_batch_transition["action"]], dim=0 + ) + left_batch_transitions["reward"] = torch.cat( + [left_batch_transitions["reward"], right_batch_transition["reward"]], dim=0 + ) + + # Concatenate next_state fields + left_batch_transitions["next_state"] = { + key: torch.cat( + [left_batch_transitions["next_state"][key], right_batch_transition["next_state"][key]], + dim=0, + ) + for key in left_batch_transitions["next_state"] + } + + # Concatenate done and truncated fields + left_batch_transitions["done"] = torch.cat( + [left_batch_transitions["done"], right_batch_transition["done"]], dim=0 + ) + left_batch_transitions["truncated"] = torch.cat( + [left_batch_transitions["truncated"], right_batch_transition["truncated"]], + dim=0, + ) + + # Handle complementary_info + left_info = left_batch_transitions.get("complementary_info") + right_info = right_batch_transition.get("complementary_info") + + # Only process if right_info exists + if right_info is not None: + # Initialize left complementary_info if needed + if left_info is None: + left_batch_transitions["complementary_info"] = right_info + else: + # Concatenate each field + for key in right_info: + if key in left_info: + left_info[key] = torch.cat([left_info[key], right_info[key]], dim=0) + else: + left_info[key] = right_info[key] + + return left_batch_transitions diff --git a/lerobot/common/utils/control_utils.py b/lerobot/common/utils/control_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8ba382dd84ba012c1be18ccb3106918f9cd7be30 --- /dev/null +++ b/lerobot/common/utils/control_utils.py @@ -0,0 +1,215 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +######################################################################################## +# Utilities +######################################################################################## + + +import logging +import traceback +from contextlib import nullcontext +from copy import copy +from functools import cache + +import numpy as np +import torch +from deepdiff import DeepDiff +from termcolor import colored + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.utils import DEFAULT_FEATURES +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.robots import Robot + + +def log_control_info(robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None): + log_items = [] + if episode_index is not None: + log_items.append(f"ep:{episode_index}") + if frame_index is not None: + log_items.append(f"frame:{frame_index}") + + def log_dt(shortname, dt_val_s): + nonlocal log_items, fps + info_str = f"{shortname}:{dt_val_s * 1000:5.2f} ({1 / dt_val_s:3.1f}hz)" + if fps is not None: + actual_fps = 1 / dt_val_s + if actual_fps < fps - 1: + info_str = colored(info_str, "yellow") + log_items.append(info_str) + + # total step time displayed in milliseconds and its frequency + log_dt("dt", dt_s) + + # TODO(aliberts): move robot-specific logs logic in robot.print_logs() + if not robot.robot_type.startswith("stretch"): + for name in robot.leader_arms: + key = f"read_leader_{name}_pos_dt_s" + if key in robot.logs: + log_dt("dtRlead", robot.logs[key]) + + for name in robot.follower_arms: + key = f"write_follower_{name}_goal_pos_dt_s" + if key in robot.logs: + log_dt("dtWfoll", robot.logs[key]) + + key = f"read_follower_{name}_pos_dt_s" + if key in robot.logs: + log_dt("dtRfoll", robot.logs[key]) + + for name in robot.cameras: + key = f"read_camera_{name}_dt_s" + if key in robot.logs: + log_dt(f"dtR{name}", robot.logs[key]) + + info_str = " ".join(log_items) + logging.info(info_str) + + +@cache +def is_headless(): + """Detects if python is running without a monitor.""" + try: + import pynput # noqa + + return False + except Exception: + print( + "Error trying to import pynput. Switching to headless mode. " + "As a result, the video stream from the cameras won't be shown, " + "and you won't be able to change the control flow with keyboards. " + "For more info, see traceback below.\n" + ) + traceback.print_exc() + print() + return True + + +def predict_action( + observation: dict[str, np.ndarray], + policy: PreTrainedPolicy, + device: torch.device, + use_amp: bool, + task: str | None = None, + robot_type: str | None = None, +): + observation = copy(observation) + with ( + torch.inference_mode(), + torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(), + ): + # Convert to pytorch format: channel first and float32 in [0,1] with batch dimension + for name in observation: + observation[name] = torch.from_numpy(observation[name]) + if "image" in name: + observation[name] = observation[name].type(torch.float32) / 255 + observation[name] = observation[name].permute(2, 0, 1).contiguous() + observation[name] = observation[name].unsqueeze(0) + observation[name] = observation[name].to(device) + + observation["task"] = task if task else "" + observation["robot_type"] = robot_type if robot_type else "" + + # Compute the next action with the policy + # based on the current observation + action = policy.select_action(observation) + + # Remove batch dimension + action = action.squeeze(0) + + # Move to cpu, if not already the case + action = action.to("cpu") + + return action + + +def init_keyboard_listener(): + # Allow to exit early while recording an episode or resetting the environment, + # by tapping the right arrow key '->'. This might require a sudo permission + # to allow your terminal to monitor keyboard events. + events = {} + events["exit_early"] = False + events["rerecord_episode"] = False + events["stop_recording"] = False + + if is_headless(): + logging.warning( + "Headless environment detected. On-screen cameras display and keyboard inputs will not be available." + ) + listener = None + return listener, events + + # Only import pynput if not in a headless environment + from pynput import keyboard + + def on_press(key): + try: + if key == keyboard.Key.right: + print("Right arrow key pressed. Exiting loop...") + events["exit_early"] = True + elif key == keyboard.Key.left: + print("Left arrow key pressed. Exiting loop and rerecord the last episode...") + events["rerecord_episode"] = True + events["exit_early"] = True + elif key == keyboard.Key.esc: + print("Escape key pressed. Stopping data recording...") + events["stop_recording"] = True + events["exit_early"] = True + except Exception as e: + print(f"Error handling key press: {e}") + + listener = keyboard.Listener(on_press=on_press) + listener.start() + + return listener, events + + +def sanity_check_dataset_name(repo_id, policy_cfg): + _, dataset_name = repo_id.split("/") + # either repo_id doesnt start with "eval_" and there is no policy + # or repo_id starts with "eval_" and there is a policy + + # Check if dataset_name starts with "eval_" but policy is missing + if dataset_name.startswith("eval_") and policy_cfg is None: + raise ValueError( + f"Your dataset name begins with 'eval_' ({dataset_name}), but no policy is provided ({policy_cfg.type})." + ) + + # Check if dataset_name does not start with "eval_" but policy is provided + if not dataset_name.startswith("eval_") and policy_cfg is not None: + raise ValueError( + f"Your dataset name does not begin with 'eval_' ({dataset_name}), but a policy is provided ({policy_cfg.type})." + ) + + +def sanity_check_dataset_robot_compatibility( + dataset: LeRobotDataset, robot: Robot, fps: int, features: dict +) -> None: + fields = [ + ("robot_type", dataset.meta.robot_type, robot.robot_type), + ("fps", dataset.fps, fps), + ("features", dataset.features, {**features, **DEFAULT_FEATURES}), + ] + + mismatches = [] + for field, dataset_value, present_value in fields: + diff = DeepDiff(dataset_value, present_value, exclude_regex_paths=[r".*\['info'\]$"]) + if diff: + mismatches.append(f"{field}: expected {present_value}, got {dataset_value}") + + if mismatches: + raise ValueError( + "Dataset metadata compatibility check failed with mismatches:\n" + "\n".join(mismatches) + ) diff --git a/lerobot/common/utils/encoding_utils.py b/lerobot/common/utils/encoding_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..15365855a41de0bd6d3c51212908d2a7db445f15 --- /dev/null +++ b/lerobot/common/utils/encoding_utils.py @@ -0,0 +1,67 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def encode_sign_magnitude(value: int, sign_bit_index: int): + """ + https://en.wikipedia.org/wiki/Signed_number_representations#Sign%E2%80%93magnitude + """ + max_magnitude = (1 << sign_bit_index) - 1 + magnitude = abs(value) + if magnitude > max_magnitude: + raise ValueError(f"Magnitude {magnitude} exceeds {max_magnitude} (max for {sign_bit_index=})") + + direction_bit = 1 if value < 0 else 0 + return (direction_bit << sign_bit_index) | magnitude + + +def decode_sign_magnitude(encoded_value: int, sign_bit_index: int): + """ + https://en.wikipedia.org/wiki/Signed_number_representations#Sign%E2%80%93magnitude + """ + direction_bit = (encoded_value >> sign_bit_index) & 1 + magnitude_mask = (1 << sign_bit_index) - 1 + magnitude = encoded_value & magnitude_mask + return -magnitude if direction_bit else magnitude + + +def encode_twos_complement(value: int, n_bytes: int): + """ + https://en.wikipedia.org/wiki/Signed_number_representations#Two%27s_complement + """ + + bit_width = n_bytes * 8 + min_val = -(1 << (bit_width - 1)) + max_val = (1 << (bit_width - 1)) - 1 + + if not (min_val <= value <= max_val): + raise ValueError( + f"Value {value} out of range for {n_bytes}-byte two's complement: [{min_val}, {max_val}]" + ) + + if value >= 0: + return value + + return (1 << bit_width) + value + + +def decode_twos_complement(value: int, n_bytes: int) -> int: + """ + https://en.wikipedia.org/wiki/Signed_number_representations#Two%27s_complement + """ + bits = n_bytes * 8 + sign_bit = 1 << (bits - 1) + if value & sign_bit: + value -= 1 << bits + return value diff --git a/lerobot/common/utils/hub.py b/lerobot/common/utils/hub.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a7ca0d8ff9e2788434455837690715adab2d4e --- /dev/null +++ b/lerobot/common/utils/hub.py @@ -0,0 +1,202 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import Any, Type, TypeVar + +from huggingface_hub import HfApi +from huggingface_hub.utils import validate_hf_hub_args + +T = TypeVar("T", bound="HubMixin") + + +class HubMixin: + """ + A Mixin containing the functionality to push an object to the hub. + + This is similar to huggingface_hub.ModelHubMixin but is lighter and makes less assumptions about its + subclasses (in particular, the fact that it's not necessarily a model). + + The inheriting classes must implement '_save_pretrained' and 'from_pretrained'. + """ + + def save_pretrained( + self, + save_directory: str | Path, + *, + repo_id: str | None = None, + push_to_hub: bool = False, + card_kwargs: dict[str, Any] | None = None, + **push_to_hub_kwargs, + ) -> str | None: + """ + Save object in local directory. + + Args: + save_directory (`str` or `Path`): + Path to directory in which the object will be saved. + push_to_hub (`bool`, *optional*, defaults to `False`): + Whether or not to push your object to the Huggingface Hub after saving it. + repo_id (`str`, *optional*): + ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to the folder name if + not provided. + card_kwargs (`Dict[str, Any]`, *optional*): + Additional arguments passed to the card template to customize the card. + push_to_hub_kwargs: + Additional key word arguments passed along to the [`~HubMixin.push_to_hub`] method. + Returns: + `str` or `None`: url of the commit on the Hub if `push_to_hub=True`, `None` otherwise. + """ + save_directory = Path(save_directory) + save_directory.mkdir(parents=True, exist_ok=True) + + # save object (weights, files, etc.) + self._save_pretrained(save_directory) + + # push to the Hub if required + if push_to_hub: + if repo_id is None: + repo_id = save_directory.name # Defaults to `save_directory` name + return self.push_to_hub(repo_id=repo_id, card_kwargs=card_kwargs, **push_to_hub_kwargs) + return None + + def _save_pretrained(self, save_directory: Path) -> None: + """ + Overwrite this method in subclass to define how to save your object. + + Args: + save_directory (`str` or `Path`): + Path to directory in which the object files will be saved. + """ + raise NotImplementedError + + @classmethod + @validate_hf_hub_args + def from_pretrained( + cls: Type[T], + pretrained_name_or_path: str | Path, + *, + force_download: bool = False, + resume_download: bool | None = None, + proxies: dict | None = None, + token: str | bool | None = None, + cache_dir: str | Path | None = None, + local_files_only: bool = False, + revision: str | None = None, + **kwargs, + ) -> T: + """ + Download the object from the Huggingface Hub and instantiate it. + + Args: + pretrained_name_or_path (`str`, `Path`): + - Either the `repo_id` (string) of the object hosted on the Hub, e.g. `lerobot/diffusion_pusht`. + - Or a path to a `directory` containing the object files saved using `.save_pretrained`, + e.g., `../path/to/my_model_directory/`. + revision (`str`, *optional*): + Revision on the Hub. Can be a branch name, a git tag or any commit id. + Defaults to the latest commit on `main` branch. + force_download (`bool`, *optional*, defaults to `False`): + Whether to force (re-)downloading the files from the Hub, overriding the existing cache. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on every request. + token (`str` or `bool`, *optional*): + The token to use as HTTP bearer authorization for remote files. By default, it will use the token + cached when running `huggingface-cli login`. + cache_dir (`str`, `Path`, *optional*): + Path to the folder where cached files are stored. + local_files_only (`bool`, *optional*, defaults to `False`): + If `True`, avoid downloading the file and return the path to the local cached file if it exists. + kwargs (`Dict`, *optional*): + Additional kwargs to pass to the object during initialization. + """ + raise NotImplementedError + + @validate_hf_hub_args + def push_to_hub( + self, + repo_id: str, + *, + commit_message: str | None = None, + private: bool | None = None, + token: str | None = None, + branch: str | None = None, + create_pr: bool | None = None, + allow_patterns: list[str] | str | None = None, + ignore_patterns: list[str] | str | None = None, + delete_patterns: list[str] | str | None = None, + card_kwargs: dict[str, Any] | None = None, + ) -> str: + """ + Upload model checkpoint to the Hub. + + Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use + `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more + details. + + Args: + repo_id (`str`): + ID of the repository to push to (example: `"username/my-model"`). + commit_message (`str`, *optional*): + Message to commit while pushing. + private (`bool`, *optional*): + Whether the repository created should be private. + If `None` (default), the repo will be public unless the organization's default is private. + token (`str`, *optional*): + The token to use as HTTP bearer authorization for remote files. By default, it will use the token + cached when running `huggingface-cli login`. + branch (`str`, *optional*): + The git branch on which to push the model. This defaults to `"main"`. + create_pr (`boolean`, *optional*): + Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`. + allow_patterns (`List[str]` or `str`, *optional*): + If provided, only files matching at least one pattern are pushed. + ignore_patterns (`List[str]` or `str`, *optional*): + If provided, files matching any of the patterns are not pushed. + delete_patterns (`List[str]` or `str`, *optional*): + If provided, remote files matching any of the patterns will be deleted from the repo. + card_kwargs (`Dict[str, Any]`, *optional*): + Additional arguments passed to the card template to customize the card. + + Returns: + The url of the commit of your object in the given repository. + """ + api = HfApi(token=token) + repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id + + if commit_message is None: + if "Policy" in self.__class__.__name__: + commit_message = "Upload policy" + elif "Config" in self.__class__.__name__: + commit_message = "Upload config" + else: + commit_message = f"Upload {self.__class__.__name__}" + + # Push the files to the repo in a single commit + with TemporaryDirectory(ignore_cleanup_errors=True) as tmp: + saved_path = Path(tmp) / repo_id + self.save_pretrained(saved_path, card_kwargs=card_kwargs) + return api.upload_folder( + repo_id=repo_id, + repo_type="model", + folder_path=saved_path, + commit_message=commit_message, + revision=branch, + create_pr=create_pr, + allow_patterns=allow_patterns, + ignore_patterns=ignore_patterns, + delete_patterns=delete_patterns, + ) diff --git a/lerobot/common/utils/import_utils.py b/lerobot/common/utils/import_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..28c05470a8cd3d9377ea93a1b9d2f789e571bdfc --- /dev/null +++ b/lerobot/common/utils/import_utils.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib +import logging + + +def is_package_available(pkg_name: str, return_version: bool = False) -> tuple[bool, str] | bool: + """Copied from https://github.com/huggingface/transformers/blob/main/src/transformers/utils/import_utils.py + Check if the package spec exists and grab its version to avoid importing a local directory. + **Note:** this doesn't work for all packages. + """ + package_exists = importlib.util.find_spec(pkg_name) is not None + package_version = "N/A" + if package_exists: + try: + # Primary method to get the package version + package_version = importlib.metadata.version(pkg_name) + + except importlib.metadata.PackageNotFoundError: + # Fallback method: Only for "torch" and versions containing "dev" + if pkg_name == "torch": + try: + package = importlib.import_module(pkg_name) + temp_version = getattr(package, "__version__", "N/A") + # Check if the version contains "dev" + if "dev" in temp_version: + package_version = temp_version + package_exists = True + else: + package_exists = False + except ImportError: + # If the package can't be imported, it's not available + package_exists = False + elif pkg_name == "grpc": + package = importlib.import_module(pkg_name) + package_version = getattr(package, "__version__", "N/A") + else: + # For packages other than "torch", don't attempt the fallback and set as not available + package_exists = False + logging.debug(f"Detected {pkg_name} version: {package_version}") + if return_version: + return package_exists, package_version + else: + return package_exists + + +_torch_available, _torch_version = is_package_available("torch", return_version=True) +_gym_xarm_available = is_package_available("gym_xarm") +_gym_aloha_available = is_package_available("gym_aloha") +_gym_pusht_available = is_package_available("gym_pusht") diff --git a/lerobot/common/utils/io_utils.py b/lerobot/common/utils/io_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1226772c97f4c9f62047d80a500704e6c17673f0 --- /dev/null +++ b/lerobot/common/utils/io_utils.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +import warnings +from pathlib import Path +from typing import TypeVar + +import imageio + +JsonLike = str | int | float | bool | None | list["JsonLike"] | dict[str, "JsonLike"] | tuple["JsonLike", ...] +T = TypeVar("T", bound=JsonLike) + + +def write_video(video_path, stacked_frames, fps): + # Filter out DeprecationWarnings raised from pkg_resources + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", "pkg_resources is deprecated as an API", category=DeprecationWarning + ) + imageio.mimsave(video_path, stacked_frames, fps=fps) + + +def deserialize_json_into_object(fpath: Path, obj: T) -> T: + """ + Loads the JSON data from `fpath` and recursively fills `obj` with the + corresponding values (strictly matching structure and types). + Tuples in `obj` are expected to be lists in the JSON data, which will be + converted back into tuples. + """ + with open(fpath, encoding="utf-8") as f: + data = json.load(f) + + def _deserialize(target, source): + """ + Recursively overwrite the structure in `target` with data from `source`, + performing strict checks on structure and type. + Returns the updated version of `target` (especially important for tuples). + """ + + # If the target is a dictionary, source must be a dictionary as well. + if isinstance(target, dict): + if not isinstance(source, dict): + raise TypeError(f"Type mismatch: expected dict, got {type(source)}") + + # Check that they have exactly the same set of keys. + if target.keys() != source.keys(): + raise ValueError( + f"Dictionary keys do not match.\nExpected: {target.keys()}, got: {source.keys()}" + ) + + # Recursively update each key. + for k in target: + target[k] = _deserialize(target[k], source[k]) + + return target + + # If the target is a list, source must be a list as well. + elif isinstance(target, list): + if not isinstance(source, list): + raise TypeError(f"Type mismatch: expected list, got {type(source)}") + + # Check length + if len(target) != len(source): + raise ValueError(f"List length mismatch: expected {len(target)}, got {len(source)}") + + # Recursively update each element. + for i in range(len(target)): + target[i] = _deserialize(target[i], source[i]) + + return target + + # If the target is a tuple, the source must be a list in JSON, + # which we'll convert back to a tuple. + elif isinstance(target, tuple): + if not isinstance(source, list): + raise TypeError(f"Type mismatch: expected list (for tuple), got {type(source)}") + + if len(target) != len(source): + raise ValueError(f"Tuple length mismatch: expected {len(target)}, got {len(source)}") + + # Convert each element, forming a new tuple. + converted_items = [] + for t_item, s_item in zip(target, source, strict=False): + converted_items.append(_deserialize(t_item, s_item)) + + # Return a brand new tuple (tuples are immutable in Python). + return tuple(converted_items) + + # Otherwise, we're dealing with a "primitive" (int, float, str, bool, None). + else: + # Check the exact type. If these must match 1:1, do: + if type(target) is not type(source): + raise TypeError(f"Type mismatch: expected {type(target)}, got {type(source)}") + return source + + # Perform the in-place/recursive deserialization + updated_obj = _deserialize(obj, data) + return updated_obj diff --git a/lerobot/common/utils/logging_utils.py b/lerobot/common/utils/logging_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8567943aa25a7764e3e2cbd4be72e45bd20a7d61 --- /dev/null +++ b/lerobot/common/utils/logging_utils.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any + +from lerobot.common.utils.utils import format_big_number + + +class AverageMeter: + """ + Computes and stores the average and current value + Adapted from https://github.com/pytorch/examples/blob/main/imagenet/main.py + """ + + def __init__(self, name: str, fmt: str = ":f"): + self.name = name + self.fmt = fmt + self.reset() + + def reset(self) -> None: + self.val = 0.0 + self.avg = 0.0 + self.sum = 0.0 + self.count = 0.0 + + def update(self, val: float, n: int = 1) -> None: + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = "{name}:{avg" + self.fmt + "}" + return fmtstr.format(**self.__dict__) + + +class MetricsTracker: + """ + A helper class to track and log metrics over time. + + Usage pattern: + + ```python + # initialize, potentially with non-zero initial step (e.g. if resuming run) + metrics = {"loss": AverageMeter("loss", ":.3f")} + train_metrics = MetricsTracker(cfg, dataset, metrics, initial_step=step) + + # update metrics derived from step (samples, episodes, epochs) at each training step + train_metrics.step() + + # update various metrics + loss = policy.forward(batch) + train_metrics.loss = loss + + # display current metrics + logging.info(train_metrics) + + # export for wandb + wandb.log(train_metrics.to_dict()) + + # reset averages after logging + train_metrics.reset_averages() + ``` + """ + + __keys__ = [ + "_batch_size", + "_num_frames", + "_avg_samples_per_ep", + "metrics", + "steps", + "samples", + "episodes", + "epochs", + ] + + def __init__( + self, + batch_size: int, + num_frames: int, + num_episodes: int, + metrics: dict[str, AverageMeter], + initial_step: int = 0, + ): + self.__dict__.update(dict.fromkeys(self.__keys__)) + self._batch_size = batch_size + self._num_frames = num_frames + self._avg_samples_per_ep = num_frames / num_episodes + self.metrics = metrics + + self.steps = initial_step + # A sample is an (observation,action) pair, where observation and action + # can be on multiple timestamps. In a batch, we have `batch_size` number of samples. + self.samples = self.steps * self._batch_size + self.episodes = self.samples / self._avg_samples_per_ep + self.epochs = self.samples / self._num_frames + + def __getattr__(self, name: str) -> int | dict[str, AverageMeter] | AverageMeter | Any: + if name in self.__dict__: + return self.__dict__[name] + elif name in self.metrics: + return self.metrics[name] + else: + raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'") + + def __setattr__(self, name: str, value: Any) -> None: + if name in self.__dict__: + super().__setattr__(name, value) + elif name in self.metrics: + self.metrics[name].update(value) + else: + raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'") + + def step(self) -> None: + """ + Updates metrics that depend on 'step' for one step. + """ + self.steps += 1 + self.samples += self._batch_size + self.episodes = self.samples / self._avg_samples_per_ep + self.epochs = self.samples / self._num_frames + + def __str__(self) -> str: + display_list = [ + f"step:{format_big_number(self.steps)}", + # number of samples seen during training + f"smpl:{format_big_number(self.samples)}", + # number of episodes seen during training + f"ep:{format_big_number(self.episodes)}", + # number of time all unique samples are seen + f"epch:{self.epochs:.2f}", + *[str(m) for m in self.metrics.values()], + ] + return " ".join(display_list) + + def to_dict(self, use_avg: bool = True) -> dict[str, int | float]: + """ + Returns the current metric values (or averages if `use_avg=True`) as a dict. + """ + return { + "steps": self.steps, + "samples": self.samples, + "episodes": self.episodes, + "epochs": self.epochs, + **{k: m.avg if use_avg else m.val for k, m in self.metrics.items()}, + } + + def reset_averages(self) -> None: + """Resets average meters.""" + for m in self.metrics.values(): + m.reset() diff --git a/lerobot/common/utils/process.py b/lerobot/common/utils/process.py new file mode 100644 index 0000000000000000000000000000000000000000..96d10b64463fa430fedd6885974ebb5dfc6d3bf8 --- /dev/null +++ b/lerobot/common/utils/process.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import signal +import sys + + +class ProcessSignalHandler: + """Utility class to attach graceful shutdown signal handlers. + + The class exposes a shutdown_event attribute that is set when a shutdown + signal is received. A counter tracks how many shutdown signals have been + caught. On the second signal the process exits with status 1. + """ + + _SUPPORTED_SIGNALS = ("SIGINT", "SIGTERM", "SIGHUP", "SIGQUIT") + + def __init__(self, use_threads: bool, display_pid: bool = False): + # TODO: Check if we can use Event from threading since Event from + # multiprocessing is the a clone of threading.Event. + # https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Event + if use_threads: + from threading import Event + else: + from multiprocessing import Event + + self.shutdown_event = Event() + self._counter: int = 0 + self._display_pid = display_pid + + self._register_handlers() + + @property + def counter(self) -> int: # pragma: no cover – simple accessor + """Number of shutdown signals that have been intercepted.""" + return self._counter + + def _register_handlers(self): + """Attach the internal _signal_handler to a subset of POSIX signals.""" + + def _signal_handler(signum, frame): + pid_str = "" + if self._display_pid: + pid_str = f"[PID: {os.getpid()}]" + logging.info(f"{pid_str} Shutdown signal {signum} received. Cleaning up…") + self.shutdown_event.set() + self._counter += 1 + + # On a second Ctrl-C (or any supported signal) force the exit to + # mimic the previous behaviour while giving the caller one chance to + # shutdown gracefully. + # TODO: Investigate if we need it later + if self._counter > 1: + logging.info("Force shutdown") + sys.exit(1) + + for sig_name in self._SUPPORTED_SIGNALS: + sig = getattr(signal, sig_name, None) + if sig is None: + # The signal is not available on this platform (Windows for + # instance does not provide SIGHUP, SIGQUIT…). Skip it. + continue + try: + signal.signal(sig, _signal_handler) + except (ValueError, OSError): # pragma: no cover – unlikely but safe + # Signal not supported or we are in a non-main thread. + continue diff --git a/lerobot/common/utils/queue.py b/lerobot/common/utils/queue.py new file mode 100644 index 0000000000000000000000000000000000000000..b0635507438c21863c46847a3602908a2e06f412 --- /dev/null +++ b/lerobot/common/utils/queue.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from queue import Empty +from typing import Any + +from torch.multiprocessing import Queue + + +def get_last_item_from_queue(queue: Queue, block=True, timeout: float = 0.1) -> Any: + if block: + try: + item = queue.get(timeout=timeout) + except Empty: + return None + else: + item = None + + # Drain queue and keep only the most recent parameters + try: + while True: + item = queue.get_nowait() + except Empty: + pass + + return item diff --git a/lerobot/common/utils/random_utils.py b/lerobot/common/utils/random_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d89470e34c59bc91eadc353c75920e48e7bab440 --- /dev/null +++ b/lerobot/common/utils/random_utils.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import random +from contextlib import contextmanager +from pathlib import Path +from typing import Any, Generator + +import numpy as np +import torch +from safetensors.torch import load_file, save_file + +from lerobot.common.constants import RNG_STATE +from lerobot.common.datasets.utils import flatten_dict, unflatten_dict + + +def serialize_python_rng_state() -> dict[str, torch.Tensor]: + """ + Returns the rng state for `random` in the form of a flat dict[str, torch.Tensor] to be saved using + `safetensors.save_file()` or `torch.save()`. + """ + py_state = random.getstate() + return { + "py_rng_version": torch.tensor([py_state[0]], dtype=torch.int64), + "py_rng_state": torch.tensor(py_state[1], dtype=torch.int64), + } + + +def deserialize_python_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None: + """ + Restores the rng state for `random` from a dictionary produced by `serialize_python_rng_state()`. + """ + py_state = (rng_state_dict["py_rng_version"].item(), tuple(rng_state_dict["py_rng_state"].tolist()), None) + random.setstate(py_state) + + +def serialize_numpy_rng_state() -> dict[str, torch.Tensor]: + """ + Returns the rng state for `numpy` in the form of a flat dict[str, torch.Tensor] to be saved using + `safetensors.save_file()` or `torch.save()`. + """ + np_state = np.random.get_state() + # Ensure no breaking changes from numpy + assert np_state[0] == "MT19937" + return { + "np_rng_state_values": torch.tensor(np_state[1], dtype=torch.int64), + "np_rng_state_index": torch.tensor([np_state[2]], dtype=torch.int64), + "np_rng_has_gauss": torch.tensor([np_state[3]], dtype=torch.int64), + "np_rng_cached_gaussian": torch.tensor([np_state[4]], dtype=torch.float32), + } + + +def deserialize_numpy_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None: + """ + Restores the rng state for `numpy` from a dictionary produced by `serialize_numpy_rng_state()`. + """ + np_state = ( + "MT19937", + rng_state_dict["np_rng_state_values"].numpy(), + rng_state_dict["np_rng_state_index"].item(), + rng_state_dict["np_rng_has_gauss"].item(), + rng_state_dict["np_rng_cached_gaussian"].item(), + ) + np.random.set_state(np_state) + + +def serialize_torch_rng_state() -> dict[str, torch.Tensor]: + """ + Returns the rng state for `torch` in the form of a flat dict[str, torch.Tensor] to be saved using + `safetensors.save_file()` or `torch.save()`. + """ + torch_rng_state_dict = {"torch_rng_state": torch.get_rng_state()} + if torch.cuda.is_available(): + torch_rng_state_dict["torch_cuda_rng_state"] = torch.cuda.get_rng_state() + return torch_rng_state_dict + + +def deserialize_torch_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None: + """ + Restores the rng state for `torch` from a dictionary produced by `serialize_torch_rng_state()`. + """ + torch.set_rng_state(rng_state_dict["torch_rng_state"]) + if torch.cuda.is_available() and "torch_cuda_rng_state" in rng_state_dict: + torch.cuda.set_rng_state(rng_state_dict["torch_cuda_rng_state"]) + + +def serialize_rng_state() -> dict[str, torch.Tensor]: + """ + Returns the rng state for `random`, `numpy`, and `torch`, in the form of a flat + dict[str, torch.Tensor] to be saved using `safetensors.save_file()` `torch.save()`. + """ + py_rng_state_dict = serialize_python_rng_state() + np_rng_state_dict = serialize_numpy_rng_state() + torch_rng_state_dict = serialize_torch_rng_state() + + return { + **py_rng_state_dict, + **np_rng_state_dict, + **torch_rng_state_dict, + } + + +def deserialize_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> None: + """ + Restores the rng state for `random`, `numpy`, and `torch` from a dictionary produced by + `serialize_rng_state()`. + """ + py_rng_state_dict = {k: v for k, v in rng_state_dict.items() if k.startswith("py")} + np_rng_state_dict = {k: v for k, v in rng_state_dict.items() if k.startswith("np")} + torch_rng_state_dict = {k: v for k, v in rng_state_dict.items() if k.startswith("torch")} + + deserialize_python_rng_state(py_rng_state_dict) + deserialize_numpy_rng_state(np_rng_state_dict) + deserialize_torch_rng_state(torch_rng_state_dict) + + +def save_rng_state(save_dir: Path) -> None: + rng_state_dict = serialize_rng_state() + flat_rng_state_dict = flatten_dict(rng_state_dict) + save_file(flat_rng_state_dict, save_dir / RNG_STATE) + + +def load_rng_state(save_dir: Path) -> None: + flat_rng_state_dict = load_file(save_dir / RNG_STATE) + rng_state_dict = unflatten_dict(flat_rng_state_dict) + deserialize_rng_state(rng_state_dict) + + +def get_rng_state() -> dict[str, Any]: + """Get the random state for `random`, `numpy`, and `torch`.""" + random_state_dict = { + "random_state": random.getstate(), + "numpy_random_state": np.random.get_state(), + "torch_random_state": torch.random.get_rng_state(), + } + if torch.cuda.is_available(): + random_state_dict["torch_cuda_random_state"] = torch.cuda.random.get_rng_state() + return random_state_dict + + +def set_rng_state(random_state_dict: dict[str, Any]): + """Set the random state for `random`, `numpy`, and `torch`. + + Args: + random_state_dict: A dictionary of the form returned by `get_rng_state`. + """ + random.setstate(random_state_dict["random_state"]) + np.random.set_state(random_state_dict["numpy_random_state"]) + torch.random.set_rng_state(random_state_dict["torch_random_state"]) + if torch.cuda.is_available(): + torch.cuda.random.set_rng_state(random_state_dict["torch_cuda_random_state"]) + + +def set_seed(seed) -> None: + """Set seed for reproducibility.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + +@contextmanager +def seeded_context(seed: int) -> Generator[None, None, None]: + """Set the seed when entering a context, and restore the prior random state at exit. + + Example usage: + + ``` + a = random.random() # produces some random number + with seeded_context(1337): + b = random.random() # produces some other random number + c = random.random() # produces yet another random number, but the same it would have if we never made `b` + ``` + """ + random_state_dict = get_rng_state() + set_seed(seed) + yield None + set_rng_state(random_state_dict) diff --git a/lerobot/common/utils/robot_utils.py b/lerobot/common/utils/robot_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bd8464803325672944ff5585e3466b9e8c619c30 --- /dev/null +++ b/lerobot/common/utils/robot_utils.py @@ -0,0 +1,44 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import platform +import time + + +def busy_wait(seconds): + if platform.system() == "Darwin": + # On Mac, `time.sleep` is not accurate and we need to use this while loop trick, + # but it consumes CPU cycles. + # TODO(rcadene): find an alternative: from python 11, time.sleep is precise + end_time = time.perf_counter() + seconds + while time.perf_counter() < end_time: + pass + else: + # On Linux time.sleep is accurate + if seconds > 0: + time.sleep(seconds) + + +def safe_disconnect(func): + # TODO(aliberts): Allow to pass custom exceptions + # (e.g. ThreadServiceExit, KeyboardInterrupt, SystemExit, UnpluggedError, DynamixelCommError) + def wrapper(robot, *args, **kwargs): + try: + return func(robot, *args, **kwargs) + except Exception as e: + if robot.is_connected: + robot.disconnect() + raise e + + return wrapper diff --git a/lerobot/common/utils/train_utils.py b/lerobot/common/utils/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..96a000d337747710dead3cb25ea09505bfd40295 --- /dev/null +++ b/lerobot/common/utils/train_utils.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +from pathlib import Path + +from termcolor import colored +from torch.optim import Optimizer +from torch.optim.lr_scheduler import LRScheduler + +from lerobot.common.constants import ( + CHECKPOINTS_DIR, + LAST_CHECKPOINT_LINK, + PRETRAINED_MODEL_DIR, + TRAINING_STATE_DIR, + TRAINING_STEP, +) +from lerobot.common.datasets.utils import load_json, write_json +from lerobot.common.optim.optimizers import load_optimizer_state, save_optimizer_state +from lerobot.common.optim.schedulers import load_scheduler_state, save_scheduler_state +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.utils.random_utils import load_rng_state, save_rng_state +from lerobot.configs.train import TrainPipelineConfig + + +def log_output_dir(out_dir): + logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}") + + +def get_step_identifier(step: int, total_steps: int) -> str: + num_digits = max(6, len(str(total_steps))) + return f"{step:0{num_digits}d}" + + +def get_step_checkpoint_dir(output_dir: Path, total_steps: int, step: int) -> Path: + """Returns the checkpoint sub-directory corresponding to the step number.""" + step_identifier = get_step_identifier(step, total_steps) + return output_dir / CHECKPOINTS_DIR / step_identifier + + +def save_training_step(step: int, save_dir: Path) -> None: + write_json({"step": step}, save_dir / TRAINING_STEP) + + +def load_training_step(save_dir: Path) -> int: + training_step = load_json(save_dir / TRAINING_STEP) + return training_step["step"] + + +def update_last_checkpoint(checkpoint_dir: Path) -> Path: + last_checkpoint_dir = checkpoint_dir.parent / LAST_CHECKPOINT_LINK + if last_checkpoint_dir.is_symlink(): + last_checkpoint_dir.unlink() + relative_target = checkpoint_dir.relative_to(checkpoint_dir.parent) + last_checkpoint_dir.symlink_to(relative_target) + + +def save_checkpoint( + checkpoint_dir: Path, + step: int, + cfg: TrainPipelineConfig, + policy: PreTrainedPolicy, + optimizer: Optimizer, + scheduler: LRScheduler | None = None, +) -> None: + """This function creates the following directory structure: + + 005000/ # training step at checkpoint + ├── pretrained_model/ + │ ├── config.json # policy config + │ ├── model.safetensors # policy weights + │ └── train_config.json # train config + └── training_state/ + ├── optimizer_param_groups.json # optimizer param groups + ├── optimizer_state.safetensors # optimizer state + ├── rng_state.safetensors # rng states + ├── scheduler_state.json # scheduler state + └── training_step.json # training step + + Args: + cfg (TrainPipelineConfig): The training config used for this run. + step (int): The training step at that checkpoint. + policy (PreTrainedPolicy): The policy to save. + optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None. + scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None. + """ + pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR + policy.save_pretrained(pretrained_dir) + cfg.save_pretrained(pretrained_dir) + save_training_state(checkpoint_dir, step, optimizer, scheduler) + + +def save_training_state( + checkpoint_dir: Path, + train_step: int, + optimizer: Optimizer | None = None, + scheduler: LRScheduler | None = None, +) -> None: + """ + Saves the training step, optimizer state, scheduler state, and rng state. + + Args: + save_dir (Path): The directory to save artifacts to. + train_step (int): Current training step. + optimizer (Optimizer | None, optional): The optimizer from which to save the state_dict. + Defaults to None. + scheduler (LRScheduler | None, optional): The scheduler from which to save the state_dict. + Defaults to None. + """ + save_dir = checkpoint_dir / TRAINING_STATE_DIR + save_dir.mkdir(parents=True, exist_ok=True) + save_training_step(train_step, save_dir) + save_rng_state(save_dir) + if optimizer is not None: + save_optimizer_state(optimizer, save_dir) + if scheduler is not None: + save_scheduler_state(scheduler, save_dir) + + +def load_training_state( + checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None +) -> tuple[int, Optimizer, LRScheduler | None]: + """ + Loads the training step, optimizer state, scheduler state, and rng state. + This is used to resume a training run. + + Args: + checkpoint_dir (Path): The checkpoint directory. Should contain a 'training_state' dir. + optimizer (Optimizer): The optimizer to load the state_dict to. + scheduler (LRScheduler | None): The scheduler to load the state_dict to (can be None). + + Raises: + NotADirectoryError: If 'checkpoint_dir' doesn't contain a 'training_state' dir + + Returns: + tuple[int, Optimizer, LRScheduler | None]: training step, optimizer and scheduler with their + state_dict loaded. + """ + training_state_dir = checkpoint_dir / TRAINING_STATE_DIR + if not training_state_dir.is_dir(): + raise NotADirectoryError(training_state_dir) + + load_rng_state(training_state_dir) + step = load_training_step(training_state_dir) + optimizer = load_optimizer_state(optimizer, training_state_dir) + if scheduler is not None: + scheduler = load_scheduler_state(scheduler, training_state_dir) + + return step, optimizer, scheduler diff --git a/lerobot/common/utils/transition.py b/lerobot/common/utils/transition.py new file mode 100644 index 0000000000000000000000000000000000000000..33686d16e1d1c220ee84b513e371faf9f6ed7ed8 --- /dev/null +++ b/lerobot/common/utils/transition.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TypedDict + +import torch + + +class Transition(TypedDict): + state: dict[str, torch.Tensor] + action: torch.Tensor + reward: float + next_state: dict[str, torch.Tensor] + done: bool + truncated: bool + complementary_info: dict[str, torch.Tensor | float | int] | None = None + + +def move_transition_to_device(transition: Transition, device: str = "cpu") -> Transition: + device = torch.device(device) + non_blocking = device.type == "cuda" + + # Move state tensors to device + transition["state"] = { + key: val.to(device, non_blocking=non_blocking) for key, val in transition["state"].items() + } + + # Move action to device + transition["action"] = transition["action"].to(device, non_blocking=non_blocking) + + # Move reward and done if they are tensors + if isinstance(transition["reward"], torch.Tensor): + transition["reward"] = transition["reward"].to(device, non_blocking=non_blocking) + + if isinstance(transition["done"], torch.Tensor): + transition["done"] = transition["done"].to(device, non_blocking=non_blocking) + + if isinstance(transition["truncated"], torch.Tensor): + transition["truncated"] = transition["truncated"].to(device, non_blocking=non_blocking) + + # Move next_state tensors to device + transition["next_state"] = { + key: val.to(device, non_blocking=non_blocking) for key, val in transition["next_state"].items() + } + + # Move complementary_info tensors if present + if transition.get("complementary_info") is not None: + for key, val in transition["complementary_info"].items(): + if isinstance(val, torch.Tensor): + transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking) + elif isinstance(val, (int, float, bool)): + transition["complementary_info"][key] = torch.tensor(val, device=device) + else: + raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]") + return transition + + +def move_state_dict_to_device(state_dict, device="cpu"): + """ + Recursively move all tensors in a (potentially) nested + dict/list/tuple structure to the CPU. + """ + if isinstance(state_dict, torch.Tensor): + return state_dict.to(device) + elif isinstance(state_dict, dict): + return {k: move_state_dict_to_device(v, device=device) for k, v in state_dict.items()} + elif isinstance(state_dict, list): + return [move_state_dict_to_device(v, device=device) for v in state_dict] + elif isinstance(state_dict, tuple): + return tuple(move_state_dict_to_device(v, device=device) for v in state_dict) + else: + return state_dict diff --git a/lerobot/common/utils/utils.py b/lerobot/common/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c16d10d469d941f1bcc752e80333ccd7a9d80bf6 --- /dev/null +++ b/lerobot/common/utils/utils.py @@ -0,0 +1,374 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import os +import os.path as osp +import platform +import select +import subprocess +import sys +import time +from copy import copy, deepcopy +from datetime import datetime, timezone +from pathlib import Path +from statistics import mean + +import numpy as np +import torch + + +def none_or_int(value): + if value == "None": + return None + return int(value) + + +def inside_slurm(): + """Check whether the python process was launched through slurm""" + # TODO(rcadene): return False for interactive mode `--pty bash` + return "SLURM_JOB_ID" in os.environ + + +def auto_select_torch_device() -> torch.device: + """Tries to select automatically a torch device.""" + if torch.cuda.is_available(): + logging.info("Cuda backend detected, using cuda.") + return torch.device("cuda") + elif torch.backends.mps.is_available(): + logging.info("Metal backend detected, using cuda.") + return torch.device("mps") + else: + logging.warning("No accelerated backend detected. Using default cpu, this will be slow.") + return torch.device("cpu") + + +# TODO(Steven): Remove log. log shouldn't be an argument, this should be handled by the logger level +def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device: + """Given a string, return a torch.device with checks on whether the device is available.""" + try_device = str(try_device) + match try_device: + case "cuda": + assert torch.cuda.is_available() + device = torch.device("cuda") + case "mps": + assert torch.backends.mps.is_available() + device = torch.device("mps") + case "cpu": + device = torch.device("cpu") + if log: + logging.warning("Using CPU, this will be slow.") + case _: + device = torch.device(try_device) + if log: + logging.warning(f"Using custom {try_device} device.") + + return device + + +def get_safe_dtype(dtype: torch.dtype, device: str | torch.device): + """ + mps is currently not compatible with float64 + """ + if isinstance(device, torch.device): + device = device.type + if device == "mps" and dtype == torch.float64: + return torch.float32 + else: + return dtype + + +def is_torch_device_available(try_device: str) -> bool: + try_device = str(try_device) # Ensure try_device is a string + if try_device == "cuda": + return torch.cuda.is_available() + elif try_device == "mps": + return torch.backends.mps.is_available() + elif try_device == "cpu": + return True + else: + raise ValueError(f"Unknown device {try_device}. Supported devices are: cuda, mps or cpu.") + + +def is_amp_available(device: str): + if device in ["cuda", "cpu"]: + return True + elif device == "mps": + return False + else: + raise ValueError(f"Unknown device '{device}.") + + +def init_logging(log_file: Path | None = None, display_pid: bool = False): + def custom_format(record): + dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + fnameline = f"{record.pathname}:{record.lineno}" + + # NOTE: Display PID is useful for multi-process logging. + if display_pid: + pid_str = f"[PID: {os.getpid()}]" + message = f"{record.levelname} {pid_str} {dt} {fnameline[-15:]:>15} {record.msg}" + else: + message = f"{record.levelname} {dt} {fnameline[-15:]:>15} {record.msg}" + return message + + logging.basicConfig(level=logging.INFO) + + for handler in logging.root.handlers[:]: + logging.root.removeHandler(handler) + + formatter = logging.Formatter() + formatter.format = custom_format + console_handler = logging.StreamHandler() + console_handler.setFormatter(formatter) + logging.getLogger().addHandler(console_handler) + + if log_file is not None: + # Additionally write logs to file + file_handler = logging.FileHandler(log_file) + file_handler.setFormatter(formatter) + logging.getLogger().addHandler(file_handler) + + +def format_big_number(num, precision=0): + suffixes = ["", "K", "M", "B", "T", "Q"] + divisor = 1000.0 + + for suffix in suffixes: + if abs(num) < divisor: + return f"{num:.{precision}f}{suffix}" + num /= divisor + + return num + + +def _relative_path_between(path1: Path, path2: Path) -> Path: + """Returns path1 relative to path2.""" + path1 = path1.absolute() + path2 = path2.absolute() + try: + return path1.relative_to(path2) + except ValueError: # most likely because path1 is not a subpath of path2 + common_parts = Path(osp.commonpath([path1, path2])).parts + return Path( + "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :])) + ) + + +def print_cuda_memory_usage(): + """Use this function to locate and debug memory leak.""" + import gc + + gc.collect() + # Also clear the cache if you want to fully release the memory + torch.cuda.empty_cache() + print("Current GPU Memory Allocated: {:.2f} MB".format(torch.cuda.memory_allocated(0) / 1024**2)) + print("Maximum GPU Memory Allocated: {:.2f} MB".format(torch.cuda.max_memory_allocated(0) / 1024**2)) + print("Current GPU Memory Reserved: {:.2f} MB".format(torch.cuda.memory_reserved(0) / 1024**2)) + print("Maximum GPU Memory Reserved: {:.2f} MB".format(torch.cuda.max_memory_reserved(0) / 1024**2)) + + +def capture_timestamp_utc(): + return datetime.now(timezone.utc) + + +def say(text, blocking=False): + system = platform.system() + + if system == "Darwin": + cmd = ["say", text] + + elif system == "Linux": + cmd = ["spd-say", text] + if blocking: + cmd.append("--wait") + + elif system == "Windows": + cmd = [ + "PowerShell", + "-Command", + "Add-Type -AssemblyName System.Speech; " + f"(New-Object System.Speech.Synthesis.SpeechSynthesizer).Speak('{text}')", + ] + + else: + raise RuntimeError("Unsupported operating system for text-to-speech.") + + if blocking: + subprocess.run(cmd, check=True) + else: + subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0) + + +def log_say(text, play_sounds, blocking=False): + logging.info(text) + + if play_sounds: + say(text, blocking) + + +def get_channel_first_image_shape(image_shape: tuple) -> tuple: + shape = copy(image_shape) + if shape[2] < shape[0] and shape[2] < shape[1]: # (h, w, c) -> (c, h, w) + shape = (shape[2], shape[0], shape[1]) + elif not (shape[0] < shape[1] and shape[0] < shape[2]): + raise ValueError(image_shape) + + return shape + + +def has_method(cls: object, method_name: str) -> bool: + return hasattr(cls, method_name) and callable(getattr(cls, method_name)) + + +def is_valid_numpy_dtype_string(dtype_str: str) -> bool: + """ + Return True if a given string can be converted to a numpy dtype. + """ + try: + # Attempt to convert the string to a numpy dtype + np.dtype(dtype_str) + return True + except TypeError: + # If a TypeError is raised, the string is not a valid dtype + return False + + +def enter_pressed() -> bool: + if platform.system() == "Windows": + import msvcrt + + if msvcrt.kbhit(): + key = msvcrt.getch() + return key in (b"\r", b"\n") # enter key + return False + else: + return select.select([sys.stdin], [], [], 0)[0] and sys.stdin.readline().strip() == "" + + +def move_cursor_up(lines): + """Move the cursor up by a specified number of lines.""" + print(f"\033[{lines}A", end="") + + +class TimerManager: + """ + Lightweight utility to measure elapsed time. + + Examples + -------- + ```python + # Example 1: Using context manager + timer = TimerManager("Policy", log=False) + for _ in range(3): + with timer: + time.sleep(0.01) + print(timer.last, timer.fps_avg, timer.percentile(90)) # Prints: 0.01 100.0 0.01 + ``` + + ```python + # Example 2: Using start/stop methods + timer = TimerManager("Policy", log=False) + timer.start() + time.sleep(0.01) + timer.stop() + print(timer.last, timer.fps_avg, timer.percentile(90)) # Prints: 0.01 100.0 0.01 + ``` + """ + + def __init__( + self, + label: str = "Elapsed-time", + log: bool = True, + logger: logging.Logger | None = None, + ): + self.label = label + self.log = log + self.logger = logger + self._start: float | None = None + self._history: list[float] = [] + + def __enter__(self): + return self.start() + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stop() + + def start(self): + self._start = time.perf_counter() + return self + + def stop(self) -> float: + if self._start is None: + raise RuntimeError("Timer was never started.") + elapsed = time.perf_counter() - self._start + self._history.append(elapsed) + self._start = None + if self.log: + if self.logger is not None: + self.logger.info(f"{self.label}: {elapsed:.6f} s") + else: + logging.info(f"{self.label}: {elapsed:.6f} s") + return elapsed + + def reset(self): + self._history.clear() + + @property + def last(self) -> float: + return self._history[-1] if self._history else 0.0 + + @property + def avg(self) -> float: + return mean(self._history) if self._history else 0.0 + + @property + def total(self) -> float: + return sum(self._history) + + @property + def count(self) -> int: + return len(self._history) + + @property + def history(self) -> list[float]: + return deepcopy(self._history) + + @property + def fps_history(self) -> list[float]: + return [1.0 / t for t in self._history] + + @property + def fps_last(self) -> float: + return 0.0 if self.last == 0 else 1.0 / self.last + + @property + def fps_avg(self) -> float: + return 0.0 if self.avg == 0 else 1.0 / self.avg + + def percentile(self, p: float) -> float: + """ + Return the p-th percentile of recorded times. + """ + if not self._history: + return 0.0 + return float(np.percentile(self._history, p)) + + def fps_percentile(self, p: float) -> float: + """ + FPS corresponding to the p-th percentile time. + """ + val = self.percentile(p) + return 0.0 if val == 0 else 1.0 / val diff --git a/lerobot/common/utils/visualization_utils.py b/lerobot/common/utils/visualization_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..64977adc362849a420969eb3b36af1814dd5ec9c --- /dev/null +++ b/lerobot/common/utils/visualization_utils.py @@ -0,0 +1,26 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import rerun as rr + + +def _init_rerun(session_name: str = "lerobot_control_loop") -> None: + """Initializes the Rerun SDK for visualizing the control loop.""" + batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000") + os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size + rr.init(session_name) + memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%") + rr.spawn(memory_limit=memory_limit) diff --git a/lerobot/common/utils/wandb_utils.py b/lerobot/common/utils/wandb_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bf05d85987ccf854657780eed74185cdd2513731 --- /dev/null +++ b/lerobot/common/utils/wandb_utils.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import os +import re +from glob import glob +from pathlib import Path + +from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE +from termcolor import colored + +from lerobot.common.constants import PRETRAINED_MODEL_DIR +from lerobot.configs.train import TrainPipelineConfig + + +def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str: + """Return a group name for logging. Optionally returns group name as list.""" + lst = [ + f"policy:{cfg.policy.type}", + f"seed:{cfg.seed}", + ] + if cfg.dataset is not None: + lst.append(f"dataset:{cfg.dataset.repo_id}") + if cfg.env is not None: + lst.append(f"env:{cfg.env.type}") + return lst if return_list else "-".join(lst) + + +def get_wandb_run_id_from_filesystem(log_dir: Path) -> str: + # Get the WandB run ID. + paths = glob(str(log_dir / "wandb/latest-run/run-*")) + if len(paths) != 1: + raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.") + match = re.search(r"run-([^\.]+).wandb", paths[0].split("/")[-1]) + if match is None: + raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.") + wandb_run_id = match.groups(0)[0] + return wandb_run_id + + +def get_safe_wandb_artifact_name(name: str): + """WandB artifacts don't accept ":" or "/" in their name.""" + return name.replace(":", "_").replace("/", "_") + + +class WandBLogger: + """A helper class to log object using wandb.""" + + def __init__(self, cfg: TrainPipelineConfig): + self.cfg = cfg.wandb + self.log_dir = cfg.output_dir + self.job_name = cfg.job_name + self.env_fps = cfg.env.fps if cfg.env else None + self._group = cfg_to_group(cfg) + + # Set up WandB. + os.environ["WANDB_SILENT"] = "True" + import wandb + + wandb_run_id = ( + cfg.wandb.run_id + if cfg.wandb.run_id + else get_wandb_run_id_from_filesystem(self.log_dir) + if cfg.resume + else None + ) + wandb.init( + id=wandb_run_id, + project=self.cfg.project, + entity=self.cfg.entity, + name=self.job_name, + notes=self.cfg.notes, + tags=cfg_to_group(cfg, return_list=True), + dir=self.log_dir, + config=cfg.to_dict(), + # TODO(rcadene): try set to True + save_code=False, + # TODO(rcadene): split train and eval, and run async eval with job_type="eval" + job_type="train_eval", + resume="must" if cfg.resume else None, + mode=self.cfg.mode if self.cfg.mode in ["online", "offline", "disabled"] else "online", + ) + run_id = wandb.run.id + # NOTE: We will override the cfg.wandb.run_id with the wandb run id. + # This is because we want to be able to resume the run from the wandb run id. + cfg.wandb.run_id = run_id + # Handle custom step key for rl asynchronous training. + self._wandb_custom_step_key: set[str] | None = None + print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"])) + logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}") + self._wandb = wandb + + def log_policy(self, checkpoint_dir: Path): + """Checkpoints the policy to wandb.""" + if self.cfg.disable_artifact: + return + + step_id = checkpoint_dir.name + artifact_name = f"{self._group}-{step_id}" + artifact_name = get_safe_wandb_artifact_name(artifact_name) + artifact = self._wandb.Artifact(artifact_name, type="model") + artifact.add_file(checkpoint_dir / PRETRAINED_MODEL_DIR / SAFETENSORS_SINGLE_FILE) + self._wandb.log_artifact(artifact) + + def log_dict( + self, d: dict, step: int | None = None, mode: str = "train", custom_step_key: str | None = None + ): + if mode not in {"train", "eval"}: + raise ValueError(mode) + if step is None and custom_step_key is None: + raise ValueError("Either step or custom_step_key must be provided.") + + # NOTE: This is not simple. Wandb step must always monotonically increase and it + # increases with each wandb.log call, but in the case of asynchronous RL for example, + # multiple time steps is possible. For example, the interaction step with the environment, + # the training step, the evaluation step, etc. So we need to define a custom step key + # to log the correct step for each metric. + if custom_step_key is not None: + if self._wandb_custom_step_key is None: + self._wandb_custom_step_key = set() + new_custom_key = f"{mode}/{custom_step_key}" + if new_custom_key not in self._wandb_custom_step_key: + self._wandb_custom_step_key.add(new_custom_key) + self._wandb.define_metric(new_custom_key, hidden=True) + + for k, v in d.items(): + if not isinstance(v, (int, float, str)): + logging.warning( + f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.' + ) + continue + + # Do not log the custom step key itself. + if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key: + continue + + if custom_step_key is not None: + value_custom_step = d[custom_step_key] + data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step} + self._wandb.log(data) + continue + + self._wandb.log(data={f"{mode}/{k}": v}, step=step) + + def log_video(self, video_path: str, step: int, mode: str = "train"): + if mode not in {"train", "eval"}: + raise ValueError(mode) + + wandb_video = self._wandb.Video(video_path, fps=self.env_fps, format="mp4") + self._wandb.log({f"{mode}/video": wandb_video}, step=step) diff --git a/lerobot/configs/default.py b/lerobot/configs/default.py new file mode 100644 index 0000000000000000000000000000000000000000..6cd1aee0a07a2a18fcc3b6cfdae3743eaea8efa7 --- /dev/null +++ b/lerobot/configs/default.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.common import ( + policies, # noqa: F401 +) +from lerobot.common.datasets.transforms import ImageTransformsConfig +from lerobot.common.datasets.video_utils import get_safe_default_codec + + +@dataclass +class DatasetConfig: + # You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data + # keys common between the datasets are kept. Each dataset gets and additional transform that inserts the + # "dataset_index" into the returned item. The index mapping is made according to the order in which the + # datasets are provided. + repo_id: str + # Root directory where the dataset will be stored (e.g. 'dataset/path'). + root: str | None = None + episodes: list[int] | None = None + image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig) + revision: str | None = None + use_imagenet_stats: bool = True + video_backend: str = field(default_factory=get_safe_default_codec) + + +@dataclass +class WandBConfig: + enable: bool = False + # Set to true to disable saving an artifact despite training.save_checkpoint=True + disable_artifact: bool = False + project: str = "lerobot" + entity: str | None = None + notes: str | None = None + run_id: str | None = None + mode: str | None = None # Allowed values: 'online', 'offline' 'disabled'. Defaults to 'online' + + +@dataclass +class EvalConfig: + n_episodes: int = 50 + # `batch_size` specifies the number of environments to use in a gym.vector.VectorEnv. + batch_size: int = 50 + # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing). + use_async_envs: bool = False + + def __post_init__(self): + if self.batch_size > self.n_episodes: + raise ValueError( + "The eval batch size is greater than the number of eval episodes " + f"({self.batch_size} > {self.n_episodes}). As a result, {self.batch_size} " + f"eval environments will be instantiated, but only {self.n_episodes} will be used. " + "This might significantly slow down evaluation. To fix this, you should update your command " + f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={self.batch_size}`), " + f"or lower the batch size (e.g. `eval.batch_size={self.n_episodes}`)." + ) diff --git a/lerobot/configs/eval.py b/lerobot/configs/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..653a2a7ff25866be85e4e903ce3925872e5b6e2c --- /dev/null +++ b/lerobot/configs/eval.py @@ -0,0 +1,65 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import datetime as dt +import logging +from dataclasses import dataclass, field +from pathlib import Path + +from lerobot.common import envs, policies # noqa: F401 +from lerobot.configs import parser +from lerobot.configs.default import EvalConfig +from lerobot.configs.policies import PreTrainedConfig + + +@dataclass +class EvalPipelineConfig: + # Either the repo ID of a model hosted on the Hub or a path to a directory containing weights + # saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch + # (useful for debugging). This argument is mutually exclusive with `--config`. + env: envs.EnvConfig + eval: EvalConfig = field(default_factory=EvalConfig) + policy: PreTrainedConfig | None = None + output_dir: Path | None = None + job_name: str | None = None + seed: int | None = 1000 + + def __post_init__(self): + # HACK: We parse again the cli args here to get the pretrained path if there was one. + policy_path = parser.get_path_arg("policy") + if policy_path: + cli_overrides = parser.get_cli_overrides("policy") + self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides) + self.policy.pretrained_path = policy_path + + else: + logging.warning( + "No pretrained path was provided, evaluated policy will be built from scratch (random weights)." + ) + + if not self.job_name: + if self.env is None: + self.job_name = f"{self.policy.type}" + else: + self.job_name = f"{self.env.type}_{self.policy.type}" + + if not self.output_dir: + now = dt.datetime.now() + eval_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}" + self.output_dir = Path("outputs/eval") / eval_dir + + @classmethod + def __get_path_fields__(cls) -> list[str]: + """This enables the parser to load config from the policy using `--policy.path=local/dir`""" + return ["policy"] diff --git a/lerobot/configs/parser.py b/lerobot/configs/parser.py new file mode 100644 index 0000000000000000000000000000000000000000..226494d32648b7c9766b361f1a55b406c0e7f927 --- /dev/null +++ b/lerobot/configs/parser.py @@ -0,0 +1,231 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib +import inspect +import pkgutil +import sys +from argparse import ArgumentError +from functools import wraps +from pathlib import Path +from typing import Sequence + +import draccus + +from lerobot.common.utils.utils import has_method + +PATH_KEY = "path" +PLUGIN_DISCOVERY_SUFFIX = "discover_packages_path" + + +def get_cli_overrides(field_name: str, args: Sequence[str] | None = None) -> list[str] | None: + """Parses arguments from cli at a given nested attribute level. + + For example, supposing the main script was called with: + python myscript.py --arg1=1 --arg2.subarg1=abc --arg2.subarg2=some/path + + If called during execution of myscript.py, get_cli_overrides("arg2") will return: + ["--subarg1=abc" "--subarg2=some/path"] + """ + if args is None: + args = sys.argv[1:] + attr_level_args = [] + detect_string = f"--{field_name}." + exclude_strings = (f"--{field_name}.{draccus.CHOICE_TYPE_KEY}=", f"--{field_name}.{PATH_KEY}=") + for arg in args: + if arg.startswith(detect_string) and not arg.startswith(exclude_strings): + denested_arg = f"--{arg.removeprefix(detect_string)}" + attr_level_args.append(denested_arg) + + return attr_level_args + + +def parse_arg(arg_name: str, args: Sequence[str] | None = None) -> str | None: + if args is None: + args = sys.argv[1:] + prefix = f"--{arg_name}=" + for arg in args: + if arg.startswith(prefix): + return arg[len(prefix) :] + return None + + +def parse_plugin_args(plugin_arg_suffix: str, args: Sequence[str]) -> dict: + """Parse plugin-related arguments from command-line arguments. + + This function extracts arguments from command-line arguments that match a specified suffix pattern. + It processes arguments in the format '--key=value' and returns them as a dictionary. + + Args: + plugin_arg_suffix (str): The suffix to identify plugin-related arguments. + cli_args (Sequence[str]): A sequence of command-line arguments to parse. + + Returns: + dict: A dictionary containing the parsed plugin arguments where: + - Keys are the argument names (with '--' prefix removed if present) + - Values are the corresponding argument values + + Example: + >>> args = ['--env.discover_packages_path=my_package', + ... '--other_arg=value'] + >>> parse_plugin_args('discover_packages_path', args) + {'env.discover_packages_path': 'my_package'} + """ + plugin_args = {} + for arg in args: + if "=" in arg and plugin_arg_suffix in arg: + key, value = arg.split("=", 1) + # Remove leading '--' if present + if key.startswith("--"): + key = key[2:] + plugin_args[key] = value + return plugin_args + + +class PluginLoadError(Exception): + """Raised when a plugin fails to load.""" + + +def load_plugin(plugin_path: str) -> None: + """Load and initialize a plugin from a given Python package path. + + This function attempts to load a plugin by importing its package and any submodules. + Plugin registration is expected to happen during package initialization, i.e. when + the package is imported the gym environment should be registered and the config classes + registered with their parents using the `register_subclass` decorator. + + Args: + plugin_path (str): The Python package path to the plugin (e.g. "mypackage.plugins.myplugin") + + Raises: + PluginLoadError: If the plugin cannot be loaded due to import errors or if the package path is invalid. + + Examples: + >>> load_plugin("external_plugin.core") # Loads plugin from external package + + Notes: + - The plugin package should handle its own registration during import + - All submodules in the plugin package will be imported + - Implementation follows the plugin discovery pattern from Python packaging guidelines + + See Also: + https://packaging.python.org/en/latest/guides/creating-and-discovering-plugins/ + """ + try: + package_module = importlib.import_module(plugin_path, __package__) + except (ImportError, ModuleNotFoundError) as e: + raise PluginLoadError( + f"Failed to load plugin '{plugin_path}'. Verify the path and installation: {str(e)}" + ) from e + + def iter_namespace(ns_pkg): + return pkgutil.iter_modules(ns_pkg.__path__, ns_pkg.__name__ + ".") + + try: + for _finder, pkg_name, _ispkg in iter_namespace(package_module): + importlib.import_module(pkg_name) + except ImportError as e: + raise PluginLoadError( + f"Failed to load plugin '{plugin_path}'. Verify the path and installation: {str(e)}" + ) from e + + +def get_path_arg(field_name: str, args: Sequence[str] | None = None) -> str | None: + return parse_arg(f"{field_name}.{PATH_KEY}", args) + + +def get_type_arg(field_name: str, args: Sequence[str] | None = None) -> str | None: + return parse_arg(f"{field_name}.{draccus.CHOICE_TYPE_KEY}", args) + + +def filter_arg(field_to_filter: str, args: Sequence[str] | None = None) -> list[str]: + return [arg for arg in args if not arg.startswith(f"--{field_to_filter}=")] + + +def filter_path_args(fields_to_filter: str | list[str], args: Sequence[str] | None = None) -> list[str]: + """ + Filters command-line arguments related to fields with specific path arguments. + + Args: + fields_to_filter (str | list[str]): A single str or a list of str whose arguments need to be filtered. + args (Sequence[str] | None): The sequence of command-line arguments to be filtered. + Defaults to None. + + Returns: + list[str]: A filtered list of arguments, with arguments related to the specified + fields removed. + + Raises: + ArgumentError: If both a path argument (e.g., `--field_name.path`) and a type + argument (e.g., `--field_name.type`) are specified for the same field. + """ + if isinstance(fields_to_filter, str): + fields_to_filter = [fields_to_filter] + + filtered_args = args + for field in fields_to_filter: + if get_path_arg(field, args): + if get_type_arg(field, args): + raise ArgumentError( + argument=None, + message=f"Cannot specify both --{field}.{PATH_KEY} and --{field}.{draccus.CHOICE_TYPE_KEY}", + ) + filtered_args = [arg for arg in filtered_args if not arg.startswith(f"--{field}.")] + + return filtered_args + + +def wrap(config_path: Path | None = None): + """ + HACK: Similar to draccus.wrap but does three additional things: + - Will remove '.path' arguments from CLI in order to process them later on. + - If a 'config_path' is passed and the main config class has a 'from_pretrained' method, will + initialize it from there to allow to fetch configs from the hub directly + - Will load plugins specified in the CLI arguments. These plugins will typically register + their own subclasses of config classes, so that draccus can find the right class to instantiate + from the CLI '.type' arguments + """ + + def wrapper_outer(fn): + @wraps(fn) + def wrapper_inner(*args, **kwargs): + argspec = inspect.getfullargspec(fn) + argtype = argspec.annotations[argspec.args[0]] + if len(args) > 0 and type(args[0]) is argtype: + cfg = args[0] + args = args[1:] + else: + cli_args = sys.argv[1:] + plugin_args = parse_plugin_args(PLUGIN_DISCOVERY_SUFFIX, cli_args) + for plugin_cli_arg, plugin_path in plugin_args.items(): + try: + load_plugin(plugin_path) + except PluginLoadError as e: + # add the relevant CLI arg to the error message + raise PluginLoadError(f"{e}\nFailed plugin CLI Arg: {plugin_cli_arg}") from e + cli_args = filter_arg(plugin_cli_arg, cli_args) + config_path_cli = parse_arg("config_path", cli_args) + if has_method(argtype, "__get_path_fields__"): + path_fields = argtype.__get_path_fields__() + cli_args = filter_path_args(path_fields, cli_args) + if has_method(argtype, "from_pretrained") and config_path_cli: + cli_args = filter_arg("config_path", cli_args) + cfg = argtype.from_pretrained(config_path_cli, cli_args=cli_args) + else: + cfg = draccus.parse(config_class=argtype, config_path=config_path, args=cli_args) + response = fn(cfg, *args, **kwargs) + return response + + return wrapper_inner + + return wrapper_outer diff --git a/lerobot/configs/policies.py b/lerobot/configs/policies.py new file mode 100644 index 0000000000000000000000000000000000000000..f1367ded392de46a417ccd0b45dd127d07a2f90c --- /dev/null +++ b/lerobot/configs/policies.py @@ -0,0 +1,180 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import abc +import logging +import os +from dataclasses import dataclass, field +from pathlib import Path +from typing import Type, TypeVar + +import draccus +from huggingface_hub import hf_hub_download +from huggingface_hub.constants import CONFIG_NAME +from huggingface_hub.errors import HfHubHTTPError + +from lerobot.common.optim.optimizers import OptimizerConfig +from lerobot.common.optim.schedulers import LRSchedulerConfig +from lerobot.common.utils.hub import HubMixin +from lerobot.common.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature + +# Generic variable that is either PreTrainedConfig or a subclass thereof +T = TypeVar("T", bound="PreTrainedConfig") + + +@dataclass +class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): + """ + Base configuration class for policy models. + + Args: + n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the + current step and additional steps going back). + input_shapes: A dictionary defining the shapes of the input data for the policy. + output_shapes: A dictionary defining the shapes of the output data for the policy. + input_normalization_modes: A dictionary with key representing the modality and the value specifies the + normalization mode to apply. + output_normalization_modes: Similar dictionary as `input_normalization_modes`, but to unnormalize to + the original scale. + """ + + n_obs_steps: int = 1 + normalization_mapping: dict[str, NormalizationMode] = field(default_factory=dict) + + input_features: dict[str, PolicyFeature] = field(default_factory=dict) + output_features: dict[str, PolicyFeature] = field(default_factory=dict) + + device: str | None = None # cuda | cpu | mp + # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP, + # automatic gradient scaling is used. + use_amp: bool = False + + def __post_init__(self): + self.pretrained_path = None + if not self.device or not is_torch_device_available(self.device): + auto_device = auto_select_torch_device() + logging.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.") + self.device = auto_device.type + + # Automatically deactivate AMP if necessary + if self.use_amp and not is_amp_available(self.device): + logging.warning( + f"Automatic Mixed Precision (amp) is not available on device '{self.device}'. Deactivating AMP." + ) + self.use_amp = False + + @property + def type(self) -> str: + return self.get_choice_name(self.__class__) + + @property + @abc.abstractmethod + def observation_delta_indices(self) -> list | None: + raise NotImplementedError + + @property + @abc.abstractmethod + def action_delta_indices(self) -> list | None: + raise NotImplementedError + + @property + @abc.abstractmethod + def reward_delta_indices(self) -> list | None: + raise NotImplementedError + + @abc.abstractmethod + def get_optimizer_preset(self) -> OptimizerConfig: + raise NotImplementedError + + @abc.abstractmethod + def get_scheduler_preset(self) -> LRSchedulerConfig | None: + raise NotImplementedError + + @abc.abstractmethod + def validate_features(self) -> None: + raise NotImplementedError + + @property + def robot_state_feature(self) -> PolicyFeature | None: + for _, ft in self.input_features.items(): + if ft.type is FeatureType.STATE: + return ft + return None + + @property + def env_state_feature(self) -> PolicyFeature | None: + for _, ft in self.input_features.items(): + if ft.type is FeatureType.ENV: + return ft + return None + + @property + def image_features(self) -> dict[str, PolicyFeature]: + return {key: ft for key, ft in self.input_features.items() if ft.type is FeatureType.VISUAL} + + @property + def action_feature(self) -> PolicyFeature | None: + for _, ft in self.output_features.items(): + if ft.type is FeatureType.ACTION: + return ft + return None + + def _save_pretrained(self, save_directory: Path) -> None: + with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"): + draccus.dump(self, f, indent=4) + + @classmethod + def from_pretrained( + cls: Type[T], + pretrained_name_or_path: str | Path, + *, + force_download: bool = False, + resume_download: bool = None, + proxies: dict | None = None, + token: str | bool | None = None, + cache_dir: str | Path | None = None, + local_files_only: bool = False, + revision: str | None = None, + **policy_kwargs, + ) -> T: + model_id = str(pretrained_name_or_path) + config_file: str | None = None + if Path(model_id).is_dir(): + if CONFIG_NAME in os.listdir(model_id): + config_file = os.path.join(model_id, CONFIG_NAME) + else: + print(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}") + else: + try: + config_file = hf_hub_download( + repo_id=model_id, + filename=CONFIG_NAME, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + ) + except HfHubHTTPError as e: + raise FileNotFoundError( + f"{CONFIG_NAME} not found on the HuggingFace Hub in {model_id}" + ) from e + + # HACK: this is very ugly, ideally we'd like to be able to do that natively with draccus + # something like --policy.path (in addition to --policy.type) + cli_overrides = policy_kwargs.pop("cli_overrides", []) + with draccus.config_type("json"): + return draccus.parse(cls, config_file, args=cli_overrides) diff --git a/lerobot/configs/train.py b/lerobot/configs/train.py new file mode 100644 index 0000000000000000000000000000000000000000..07472715bd1ff509fb22c25bd5b7107d6a7cc1b7 --- /dev/null +++ b/lerobot/configs/train.py @@ -0,0 +1,179 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import datetime as dt +import os +from dataclasses import dataclass, field +from pathlib import Path +from typing import Type + +import draccus +from huggingface_hub import hf_hub_download +from huggingface_hub.errors import HfHubHTTPError + +from lerobot.common import envs +from lerobot.common.optim import OptimizerConfig +from lerobot.common.optim.schedulers import LRSchedulerConfig +from lerobot.common.utils.hub import HubMixin +from lerobot.configs import parser +from lerobot.configs.default import DatasetConfig, EvalConfig, WandBConfig +from lerobot.configs.policies import PreTrainedConfig + +TRAIN_CONFIG_NAME = "train_config.json" + + +@dataclass +class TrainPipelineConfig(HubMixin): + dataset: DatasetConfig + env: envs.EnvConfig | None = None + policy: PreTrainedConfig | None = None + # Set `dir` to where you would like to save all of the run outputs. If you run another training session + # with the same value for `dir` its contents will be overwritten unless you set `resume` to true. + output_dir: Path | None = None + job_name: str | None = None + # Set `resume` to true to resume a previous run. In order for this to work, you will need to make sure + # `dir` is the directory of an existing run with at least one checkpoint in it. + # Note that when resuming a run, the default behavior is to use the configuration from the checkpoint, + # regardless of what's provided with the training command at the time of resumption. + resume: bool = False + # `seed` is used for training (eg: model initialization, dataset shuffling) + # AND for the evaluation environments. + seed: int | None = 1000 + # Number of workers for the dataloader. + num_workers: int = 4 + batch_size: int = 8 + steps: int = 100_000 + eval_freq: int = 20_000 + log_freq: int = 200 + save_checkpoint: bool = True + # Checkpoint is saved every `save_freq` training iterations and after the last training step. + save_freq: int = 20_000 + use_policy_training_preset: bool = True + optimizer: OptimizerConfig | None = None + scheduler: LRSchedulerConfig | None = None + eval: EvalConfig = field(default_factory=EvalConfig) + wandb: WandBConfig = field(default_factory=WandBConfig) + + def __post_init__(self): + self.checkpoint_path = None + + def validate(self): + # HACK: We parse again the cli args here to get the pretrained paths if there was some. + policy_path = parser.get_path_arg("policy") + if policy_path: + # Only load the policy config + cli_overrides = parser.get_cli_overrides("policy") + self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides) + self.policy.pretrained_path = policy_path + elif self.resume: + # The entire train config is already loaded, we just need to get the checkpoint dir + config_path = parser.parse_arg("config_path") + if not config_path: + raise ValueError( + f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}" + ) + if not Path(config_path).resolve().exists(): + raise NotADirectoryError( + f"{config_path=} is expected to be a local path. " + "Resuming from the hub is not supported for now." + ) + policy_path = Path(config_path).parent + self.policy.pretrained_path = policy_path + self.checkpoint_path = policy_path.parent + + if not self.job_name: + if self.env is None: + self.job_name = f"{self.policy.type}" + else: + self.job_name = f"{self.env.type}_{self.policy.type}" + + if not self.resume and isinstance(self.output_dir, Path) and self.output_dir.is_dir(): + raise FileExistsError( + f"Output directory {self.output_dir} already exists and resume is {self.resume}. " + f"Please change your output directory so that {self.output_dir} is not overwritten." + ) + elif not self.output_dir: + now = dt.datetime.now() + train_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}" + self.output_dir = Path("outputs/train") / train_dir + + if isinstance(self.dataset.repo_id, list): + raise NotImplementedError("LeRobotMultiDataset is not currently implemented.") + + if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None): + raise ValueError("Optimizer and Scheduler must be set when the policy presets are not used.") + elif self.use_policy_training_preset and not self.resume: + self.optimizer = self.policy.get_optimizer_preset() + self.scheduler = self.policy.get_scheduler_preset() + + @classmethod + def __get_path_fields__(cls) -> list[str]: + """This enables the parser to load config from the policy using `--policy.path=local/dir`""" + return ["policy"] + + def to_dict(self) -> dict: + return draccus.encode(self) + + def _save_pretrained(self, save_directory: Path) -> None: + with open(save_directory / TRAIN_CONFIG_NAME, "w") as f, draccus.config_type("json"): + draccus.dump(self, f, indent=4) + + @classmethod + def from_pretrained( + cls: Type["TrainPipelineConfig"], + pretrained_name_or_path: str | Path, + *, + force_download: bool = False, + resume_download: bool = None, + proxies: dict | None = None, + token: str | bool | None = None, + cache_dir: str | Path | None = None, + local_files_only: bool = False, + revision: str | None = None, + **kwargs, + ) -> "TrainPipelineConfig": + model_id = str(pretrained_name_or_path) + config_file: str | None = None + if Path(model_id).is_dir(): + if TRAIN_CONFIG_NAME in os.listdir(model_id): + config_file = os.path.join(model_id, TRAIN_CONFIG_NAME) + else: + print(f"{TRAIN_CONFIG_NAME} not found in {Path(model_id).resolve()}") + elif Path(model_id).is_file(): + config_file = model_id + else: + try: + config_file = hf_hub_download( + repo_id=model_id, + filename=TRAIN_CONFIG_NAME, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + ) + except HfHubHTTPError as e: + raise FileNotFoundError( + f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}" + ) from e + + cli_args = kwargs.pop("cli_args", []) + with draccus.config_type("json"): + return draccus.parse(cls, config_file, args=cli_args) + + +@dataclass(kw_only=True) +class TrainRLServerPipelineConfig(TrainPipelineConfig): + dataset: DatasetConfig | None = None # NOTE: In RL, we don't need an offline dataset diff --git a/lerobot/configs/types.py b/lerobot/configs/types.py new file mode 100644 index 0000000000000000000000000000000000000000..377a863a53ea3a0b813623bd1cd4b53b140d3d3b --- /dev/null +++ b/lerobot/configs/types.py @@ -0,0 +1,42 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# Note: We subclass str so that serialization is straightforward +# https://stackoverflow.com/questions/24481852/serialising-an-enum-member-to-json +from dataclasses import dataclass +from enum import Enum +from typing import Any, Protocol + + +class FeatureType(str, Enum): + STATE = "STATE" + VISUAL = "VISUAL" + ENV = "ENV" + ACTION = "ACTION" + REWARD = "REWARD" + + +class NormalizationMode(str, Enum): + MIN_MAX = "MIN_MAX" + MEAN_STD = "MEAN_STD" + IDENTITY = "IDENTITY" + + +class DictLike(Protocol): + def __getitem__(self, key: Any) -> Any: ... + + +@dataclass +class PolicyFeature: + type: FeatureType + shape: tuple diff --git a/lerobot/find_cameras.py b/lerobot/find_cameras.py new file mode 100644 index 0000000000000000000000000000000000000000..3b0c244a4e6a92a8f45aa78f8e760dab5d60b217 --- /dev/null +++ b/lerobot/find_cameras.py @@ -0,0 +1,315 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Helper to find the camera devices available in your system. + +Example: + +```shell +python -m lerobot.find_cameras +``` +""" + +# NOTE(Steven): RealSense can also be identified/opened as OpenCV cameras. If you know the camera is a RealSense, use the `lerobot.find_cameras realsense` flag to avoid confusion. +# NOTE(Steven): macOS cameras sometimes report different FPS at init time, not an issue here as we don't specify FPS when opening the cameras, but the information displayed might not be truthful. + +import argparse +import concurrent.futures +import logging +import time +from pathlib import Path +from typing import Any, Dict, List + +import numpy as np +from PIL import Image + +from lerobot.common.cameras.configs import ColorMode +from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.common.cameras.realsense.camera_realsense import RealSenseCamera +from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig + +logger = logging.getLogger(__name__) + + +def find_all_opencv_cameras() -> List[Dict[str, Any]]: + """ + Finds all available OpenCV cameras plugged into the system. + + Returns: + A list of all available OpenCV cameras with their metadata. + """ + all_opencv_cameras_info: List[Dict[str, Any]] = [] + logger.info("Searching for OpenCV cameras...") + try: + opencv_cameras = OpenCVCamera.find_cameras() + for cam_info in opencv_cameras: + all_opencv_cameras_info.append(cam_info) + logger.info(f"Found {len(opencv_cameras)} OpenCV cameras.") + except Exception as e: + logger.error(f"Error finding OpenCV cameras: {e}") + + return all_opencv_cameras_info + + +def find_all_realsense_cameras() -> List[Dict[str, Any]]: + """ + Finds all available RealSense cameras plugged into the system. + + Returns: + A list of all available RealSense cameras with their metadata. + """ + all_realsense_cameras_info: List[Dict[str, Any]] = [] + logger.info("Searching for RealSense cameras...") + try: + realsense_cameras = RealSenseCamera.find_cameras() + for cam_info in realsense_cameras: + all_realsense_cameras_info.append(cam_info) + logger.info(f"Found {len(realsense_cameras)} RealSense cameras.") + except ImportError: + logger.warning("Skipping RealSense camera search: pyrealsense2 library not found or not importable.") + except Exception as e: + logger.error(f"Error finding RealSense cameras: {e}") + + return all_realsense_cameras_info + + +def find_and_print_cameras(camera_type_filter: str | None = None) -> List[Dict[str, Any]]: + """ + Finds available cameras based on an optional filter and prints their information. + + Args: + camera_type_filter: Optional string to filter cameras ("realsense" or "opencv"). + If None, lists all cameras. + + Returns: + A list of all available cameras matching the filter, with their metadata. + """ + all_cameras_info: List[Dict[str, Any]] = [] + + if camera_type_filter: + camera_type_filter = camera_type_filter.lower() + + if camera_type_filter is None or camera_type_filter == "opencv": + all_cameras_info.extend(find_all_opencv_cameras()) + if camera_type_filter is None or camera_type_filter == "realsense": + all_cameras_info.extend(find_all_realsense_cameras()) + + if not all_cameras_info: + if camera_type_filter: + logger.warning(f"No {camera_type_filter} cameras were detected.") + else: + logger.warning("No cameras (OpenCV or RealSense) were detected.") + else: + print("\n--- Detected Cameras ---") + for i, cam_info in enumerate(all_cameras_info): + print(f"Camera #{i}:") + for key, value in cam_info.items(): + if key == "default_stream_profile" and isinstance(value, dict): + print(f" {key.replace('_', ' ').capitalize()}:") + for sub_key, sub_value in value.items(): + print(f" {sub_key.capitalize()}: {sub_value}") + else: + print(f" {key.replace('_', ' ').capitalize()}: {value}") + print("-" * 20) + return all_cameras_info + + +def save_image( + img_array: np.ndarray, + camera_identifier: str | int, + images_dir: Path, + camera_type: str, +): + """ + Saves a single image to disk using Pillow. Handles color conversion if necessary. + """ + try: + img = Image.fromarray(img_array, mode="RGB") + + safe_identifier = str(camera_identifier).replace("/", "_").replace("\\", "_") + filename_prefix = f"{camera_type.lower()}_{safe_identifier}" + filename = f"{filename_prefix}.png" + + path = images_dir / filename + path.parent.mkdir(parents=True, exist_ok=True) + img.save(str(path)) + logger.info(f"Saved image: {path}") + except Exception as e: + logger.error(f"Failed to save image for camera {camera_identifier} (type {camera_type}): {e}") + + +def create_camera_instance(cam_meta: Dict[str, Any]) -> Dict[str, Any] | None: + """Create and connect to a camera instance based on metadata.""" + cam_type = cam_meta.get("type") + cam_id = cam_meta.get("id") + instance = None + + logger.info(f"Preparing {cam_type} ID {cam_id} with default profile") + + try: + if cam_type == "OpenCV": + cv_config = OpenCVCameraConfig( + index_or_path=cam_id, + color_mode=ColorMode.RGB, + ) + instance = OpenCVCamera(cv_config) + elif cam_type == "RealSense": + rs_config = RealSenseCameraConfig( + serial_number_or_name=cam_id, + color_mode=ColorMode.RGB, + ) + instance = RealSenseCamera(rs_config) + else: + logger.warning(f"Unknown camera type: {cam_type} for ID {cam_id}. Skipping.") + return None + + if instance: + logger.info(f"Connecting to {cam_type} camera: {cam_id}...") + instance.connect(warmup=False) + return {"instance": instance, "meta": cam_meta} + except Exception as e: + logger.error(f"Failed to connect or configure {cam_type} camera {cam_id}: {e}") + if instance and instance.is_connected: + instance.disconnect() + return None + + +def process_camera_image( + cam_dict: Dict[str, Any], output_dir: Path, current_time: float +) -> concurrent.futures.Future | None: + """Capture and process an image from a single camera.""" + cam = cam_dict["instance"] + meta = cam_dict["meta"] + cam_type_str = str(meta.get("type", "unknown")) + cam_id_str = str(meta.get("id", "unknown")) + + try: + image_data = cam.read() + + return save_image( + image_data, + cam_id_str, + output_dir, + cam_type_str, + ) + except TimeoutError: + logger.warning( + f"Timeout reading from {cam_type_str} camera {cam_id_str} at time {current_time:.2f}s." + ) + except Exception as e: + logger.error(f"Error reading from {cam_type_str} camera {cam_id_str}: {e}") + return None + + +def cleanup_cameras(cameras_to_use: List[Dict[str, Any]]): + """Disconnect all cameras.""" + logger.info(f"Disconnecting {len(cameras_to_use)} cameras...") + for cam_dict in cameras_to_use: + try: + if cam_dict["instance"] and cam_dict["instance"].is_connected: + cam_dict["instance"].disconnect() + except Exception as e: + logger.error(f"Error disconnecting camera {cam_dict['meta'].get('id')}: {e}") + + +def save_images_from_all_cameras( + output_dir: Path, + record_time_s: float = 2.0, + camera_type: str | None = None, +): + """ + Connects to detected cameras (optionally filtered by type) and saves images from each. + Uses default stream profiles for width, height, and FPS. + + Args: + output_dir: Directory to save images. + record_time_s: Duration in seconds to record images. + camera_type: Optional string to filter cameras ("realsense" or "opencv"). + If None, uses all detected cameras. + """ + output_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving images to {output_dir}") + all_camera_metadata = find_and_print_cameras(camera_type_filter=camera_type) + + if not all_camera_metadata: + logger.warning("No cameras detected matching the criteria. Cannot save images.") + return + + cameras_to_use = [] + for cam_meta in all_camera_metadata: + camera_instance = create_camera_instance(cam_meta) + if camera_instance: + cameras_to_use.append(camera_instance) + + if not cameras_to_use: + logger.warning("No cameras could be connected. Aborting image save.") + return + + logger.info(f"Starting image capture for {record_time_s} seconds from {len(cameras_to_use)} cameras.") + start_time = time.perf_counter() + + with concurrent.futures.ThreadPoolExecutor(max_workers=len(cameras_to_use) * 2) as executor: + try: + while time.perf_counter() - start_time < record_time_s: + futures = [] + current_capture_time = time.perf_counter() + + for cam_dict in cameras_to_use: + future = process_camera_image(cam_dict, output_dir, current_capture_time) + if future: + futures.append(future) + + if futures: + concurrent.futures.wait(futures) + + except KeyboardInterrupt: + logger.info("Capture interrupted by user.") + finally: + print("\nFinalizing image saving...") + executor.shutdown(wait=True) + cleanup_cameras(cameras_to_use) + print(f"Image capture finished. Images saved to {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Unified camera utility script for listing cameras and capturing images." + ) + + parser.add_argument( + "camera_type", + type=str, + nargs="?", + default=None, + choices=["realsense", "opencv"], + help="Specify camera type to capture from (e.g., 'realsense', 'opencv'). Captures from all if omitted.", + ) + parser.add_argument( + "--output-dir", + type=Path, + default="outputs/captured_images", + help="Directory to save images. Default: outputs/captured_images", + ) + parser.add_argument( + "--record-time-s", + type=float, + default=6.0, + help="Time duration to attempt capturing frames. Default: 6 seconds.", + ) + args = parser.parse_args() + save_images_from_all_cameras(**vars(args)) diff --git a/lerobot/find_port.py b/lerobot/find_port.py new file mode 100644 index 0000000000000000000000000000000000000000..b96f0ec9f7807c74354c9af250919bcc3a2990da --- /dev/null +++ b/lerobot/find_port.py @@ -0,0 +1,65 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Helper to find the USB port associated with your MotorsBus. + +Example: + +```shell +python -m lerobot.find_port +``` +""" + +import platform +import time +from pathlib import Path + + +def find_available_ports(): + from serial.tools import list_ports # Part of pyserial library + + if platform.system() == "Windows": + # List COM ports using pyserial + ports = [port.device for port in list_ports.comports()] + else: # Linux/macOS + # List /dev/tty* ports for Unix-based systems + ports = [str(path) for path in Path("/dev").glob("tty*")] + return ports + + +def find_port(): + print("Finding all available ports for the MotorsBus.") + ports_before = find_available_ports() + print("Ports before disconnecting:", ports_before) + + print("Remove the USB cable from your MotorsBus and press Enter when done.") + input() # Wait for user to disconnect the device + + time.sleep(0.5) # Allow some time for port to be released + ports_after = find_available_ports() + ports_diff = list(set(ports_before) - set(ports_after)) + + if len(ports_diff) == 1: + port = ports_diff[0] + print(f"The port of this MotorsBus is '{port}'") + print("Reconnect the USB cable.") + elif len(ports_diff) == 0: + raise OSError(f"Could not detect the port. No difference was found ({ports_diff}).") + else: + raise OSError(f"Could not detect the port. More than one port was found ({ports_diff}).") + + +if __name__ == "__main__": + find_port() diff --git a/lerobot/record.py b/lerobot/record.py new file mode 100644 index 0000000000000000000000000000000000000000..56c6090e6fccb8b0d964ce614c9bc453b2ee6087 --- /dev/null +++ b/lerobot/record.py @@ -0,0 +1,335 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Records a dataset. Actions for the robot can be either generated by teleoperation or by a policy. + +Example: + +```shell +python -m lerobot.record \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.cameras="{laptop: {type: opencv, camera_index: 0, width: 640, height: 480}}" \ + --robot.id=black \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue \ + --dataset.repo_id=aliberts/record-test \ + --dataset.num_episodes=2 \ + --dataset.single_task="Grab the cube" +``` +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pathlib import Path +from pprint import pformat + +import numpy as np +import rerun as rr + +from lerobot.common.cameras import ( # noqa: F401 + CameraConfig, # noqa: F401 +) +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 +from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 +from lerobot.common.datasets.image_writer import safe_stop_image_writer +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.utils import build_dataset_frame, hw_to_dataset_features +from lerobot.common.policies.factory import make_policy +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.robots import ( # noqa: F401 + Robot, + RobotConfig, + koch_follower, + make_robot_from_config, + so100_follower, + so101_follower, +) +from lerobot.common.teleoperators import ( # noqa: F401 + Teleoperator, + TeleoperatorConfig, + make_teleoperator_from_config, +) +from lerobot.common.utils.control_utils import ( + init_keyboard_listener, + is_headless, + predict_action, + sanity_check_dataset_name, + sanity_check_dataset_robot_compatibility, +) +from lerobot.common.utils.robot_utils import busy_wait +from lerobot.common.utils.utils import ( + get_safe_torch_device, + init_logging, + log_say, +) +from lerobot.common.utils.visualization_utils import _init_rerun +from lerobot.configs import parser +from lerobot.configs.policies import PreTrainedConfig + +from .common.teleoperators import koch_leader, so100_leader, so101_leader # noqa: F401 + + +@dataclass +class DatasetRecordConfig: + # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`). + repo_id: str + # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.") + single_task: str + # Root directory where the dataset will be stored (e.g. 'dataset/path'). + root: str | Path | None = None + # Limit the frames per second. + fps: int = 30 + # Number of seconds for data recording for each episode. + episode_time_s: int | float = 60 + # Number of seconds for resetting the environment after each episode. + reset_time_s: int | float = 60 + # Number of episodes to record. + num_episodes: int = 50 + # Encode frames in the dataset into video + video: bool = True + # Upload dataset to Hugging Face hub. + push_to_hub: bool = True + # Upload on private repository on the Hugging Face hub. + private: bool = False + # Add tags to your dataset on the hub. + tags: list[str] | None = None + # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only; + # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes + # and threads depends on your system. We recommend 4 threads per camera with 0 processes. + # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses. + num_image_writer_processes: int = 0 + # Number of threads writing the frames as png images on disk, per camera. + # Too many threads might cause unstable teleoperation fps due to main thread being blocked. + # Not enough threads might cause low camera fps. + num_image_writer_threads_per_camera: int = 4 + + def __post_init__(self): + if self.single_task is None: + raise ValueError("You need to provide a task as argument in `single_task`.") + + +@dataclass +class RecordConfig: + robot: RobotConfig + dataset: DatasetRecordConfig + # Whether to control the robot with a teleoperator + teleop: TeleoperatorConfig | None = None + # Whether to control the robot with a policy + policy: PreTrainedConfig | None = None + # Display all cameras on screen + display_data: bool = False + # Use vocal synthesis to read events. + play_sounds: bool = True + # Resume recording on an existing dataset. + resume: bool = False + + def __post_init__(self): + if self.teleop is not None and self.policy is not None: + raise ValueError("Choose either a policy or a teleoperator to control the robot") + + # HACK: We parse again the cli args here to get the pretrained path if there was one. + policy_path = parser.get_path_arg("policy") + if policy_path: + cli_overrides = parser.get_cli_overrides("policy") + self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides) + self.policy.pretrained_path = policy_path + + @classmethod + def __get_path_fields__(cls) -> list[str]: + """This enables the parser to load config from the policy using `--policy.path=local/dir`""" + return ["policy"] + + +@safe_stop_image_writer +def record_loop( + robot: Robot, + events: dict, + fps: int, + dataset: LeRobotDataset | None = None, + teleop: Teleoperator | None = None, + policy: PreTrainedPolicy | None = None, + control_time_s: int | None = None, + single_task: str | None = None, + display_data: bool = False, +): + if dataset is not None and dataset.fps != fps: + raise ValueError(f"The dataset fps should be equal to requested fps ({dataset.fps} != {fps}).") + + # if policy is given it needs cleaning up + if policy is not None: + policy.reset() + + timestamp = 0 + start_episode_t = time.perf_counter() + while timestamp < control_time_s: + start_loop_t = time.perf_counter() + + observation = robot.get_observation() + + if policy is not None or dataset is not None: + observation_frame = build_dataset_frame(dataset.features, observation, prefix="observation") + + if policy is not None: + action_values = predict_action( + observation_frame, + policy, + get_safe_torch_device(policy.config.device), + policy.config.use_amp, + task=single_task, + robot_type=robot.robot_type, + ) + action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)} + else: + action = teleop.get_action() + + # Action can eventually be clipped using `max_relative_target`, + # so action actually sent is saved in the dataset. + sent_action = robot.send_action(action) + + if dataset is not None: + action_frame = build_dataset_frame(dataset.features, sent_action, prefix="action") + frame = {**observation_frame, **action_frame} + dataset.add_frame(frame, task=single_task) + + if display_data: + for obs, val in observation.items(): + if isinstance(val, float): + rr.log(f"observation.{obs}", rr.Scalar(val)) + elif isinstance(val, np.ndarray): + rr.log(f"observation.{obs}", rr.Image(val), static=True) + for act, val in action.items(): + if isinstance(val, float): + rr.log(f"action.{act}", rr.Scalar(val)) + + dt_s = time.perf_counter() - start_loop_t + busy_wait(1 / fps - dt_s) + + timestamp = time.perf_counter() - start_episode_t + if events["exit_early"]: + events["exit_early"] = False + break + + +@parser.wrap() +def record(cfg: RecordConfig) -> LeRobotDataset: + init_logging() + logging.info(pformat(asdict(cfg))) + if cfg.display_data: + _init_rerun(session_name="recording") + + robot = make_robot_from_config(cfg.robot) + teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None + + action_features = hw_to_dataset_features(robot.action_features, "action", cfg.dataset.video) + obs_features = hw_to_dataset_features(robot.observation_features, "observation", cfg.dataset.video) + dataset_features = {**action_features, **obs_features} + + if cfg.resume: + dataset = LeRobotDataset( + cfg.dataset.repo_id, + root=cfg.dataset.root, + ) + + if hasattr(robot, "cameras") and len(robot.cameras) > 0: + dataset.start_image_writer( + num_processes=cfg.dataset.num_image_writer_processes, + num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras), + ) + sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features) + else: + # Create empty dataset or load existing saved episodes + sanity_check_dataset_name(cfg.dataset.repo_id, cfg.policy) + dataset = LeRobotDataset.create( + cfg.dataset.repo_id, + cfg.dataset.fps, + root=cfg.dataset.root, + robot_type=robot.name, + features=dataset_features, + use_videos=cfg.dataset.video, + image_writer_processes=cfg.dataset.num_image_writer_processes, + image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras), + ) + + # Load pretrained policy + policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta) + + robot.connect() + if teleop is not None: + teleop.connect() + + listener, events = init_keyboard_listener() + + for recorded_episodes in range(cfg.dataset.num_episodes): + log_say(f"Recording episode {dataset.num_episodes}", cfg.play_sounds) + record_loop( + robot=robot, + events=events, + fps=cfg.dataset.fps, + teleop=teleop, + policy=policy, + dataset=dataset, + control_time_s=cfg.dataset.episode_time_s, + single_task=cfg.dataset.single_task, + display_data=cfg.display_data, + ) + + # Execute a few seconds without recording to give time to manually reset the environment + # Skip reset for the last episode to be recorded + if not events["stop_recording"] and ( + (recorded_episodes < cfg.dataset.num_episodes - 1) or events["rerecord_episode"] + ): + log_say("Reset the environment", cfg.play_sounds) + record_loop( + robot=robot, + events=events, + fps=cfg.dataset.fps, + teleop=teleop, + control_time_s=cfg.dataset.reset_time_s, + single_task=cfg.dataset.single_task, + display_data=cfg.display_data, + ) + + if events["rerecord_episode"]: + log_say("Re-record episode", cfg.play_sounds) + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue + + dataset.save_episode() + + if events["stop_recording"]: + break + + log_say("Stop recording", cfg.play_sounds, blocking=True) + + robot.disconnect() + teleop.disconnect() + + if not is_headless() and listener is not None: + listener.stop() + + if cfg.dataset.push_to_hub: + dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private) + + log_say("Exiting", cfg.play_sounds) + return dataset + + +if __name__ == "__main__": + record() diff --git a/lerobot/replay.py b/lerobot/replay.py new file mode 100644 index 0000000000000000000000000000000000000000..4672a5751ebcaa8ea4cab89ac8b74b2078e9f96c --- /dev/null +++ b/lerobot/replay.py @@ -0,0 +1,102 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Replays the actions of an episode from a dataset on a robot. + +Example: + +```shell +python -m lerobot.replay \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=black \ + --dataset.repo_id=aliberts/record-test \ + --dataset.episode=2 +``` +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pathlib import Path +from pprint import pformat + +import draccus + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.robots import ( # noqa: F401 + Robot, + RobotConfig, + koch_follower, + make_robot_from_config, + so100_follower, + so101_follower, +) +from lerobot.common.utils.robot_utils import busy_wait +from lerobot.common.utils.utils import ( + init_logging, + log_say, +) + + +@dataclass +class DatasetReplayConfig: + # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`). + repo_id: str + # Episode to replay. + episode: int + # Root directory where the dataset will be stored (e.g. 'dataset/path'). + root: str | Path | None = None + # Limit the frames per second. By default, uses the policy fps. + fps: int = 30 + + +@dataclass +class ReplayConfig: + robot: RobotConfig + dataset: DatasetReplayConfig + # Use vocal synthesis to read events. + play_sounds: bool = True + + +@draccus.wrap() +def replay(cfg: ReplayConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + + robot = make_robot_from_config(cfg.robot) + dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode]) + actions = dataset.hf_dataset.select_columns("action") + robot.connect() + + log_say("Replaying episode", cfg.play_sounds, blocking=True) + for idx in range(dataset.num_frames): + start_episode_t = time.perf_counter() + + action_array = actions[idx]["action"] + action = {} + for i, name in enumerate(dataset.features["action"]["names"]): + action[name] = action_array[i] + + robot.send_action(action) + + dt_s = time.perf_counter() - start_episode_t + busy_wait(1 / dataset.fps - dt_s) + + robot.disconnect() + + +if __name__ == "__main__": + replay() diff --git a/lerobot/scripts/display_sys_info.py b/lerobot/scripts/display_sys_info.py new file mode 100644 index 0000000000000000000000000000000000000000..913e71dcb2d485923d85eeba29341623863e167b --- /dev/null +++ b/lerobot/scripts/display_sys_info.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Use this script to get a quick summary of your system config. +It should be able to run without any of LeRobot's dependencies or LeRobot itself installed. +""" + +import platform + +HAS_HF_HUB = True +HAS_HF_DATASETS = True +HAS_NP = True +HAS_TORCH = True +HAS_LEROBOT = True + +try: + import huggingface_hub +except ImportError: + HAS_HF_HUB = False + +try: + import datasets +except ImportError: + HAS_HF_DATASETS = False + +try: + import numpy as np +except ImportError: + HAS_NP = False + +try: + import torch +except ImportError: + HAS_TORCH = False + +try: + import lerobot +except ImportError: + HAS_LEROBOT = False + + +lerobot_version = lerobot.__version__ if HAS_LEROBOT else "N/A" +hf_hub_version = huggingface_hub.__version__ if HAS_HF_HUB else "N/A" +hf_datasets_version = datasets.__version__ if HAS_HF_DATASETS else "N/A" +np_version = np.__version__ if HAS_NP else "N/A" + +torch_version = torch.__version__ if HAS_TORCH else "N/A" +torch_cuda_available = torch.cuda.is_available() if HAS_TORCH else "N/A" +cuda_version = torch._C._cuda_getCompiledVersion() if HAS_TORCH and torch.version.cuda is not None else "N/A" + + +# TODO(aliberts): refactor into an actual command `lerobot env` +def display_sys_info() -> dict: + """Run this to get basic system info to help for tracking issues & bugs.""" + info = { + "`lerobot` version": lerobot_version, + "Platform": platform.platform(), + "Python version": platform.python_version(), + "Huggingface_hub version": hf_hub_version, + "Dataset version": hf_datasets_version, + "Numpy version": np_version, + "PyTorch version (GPU?)": f"{torch_version} ({torch_cuda_available})", + "Cuda version": cuda_version, + "Using GPU in script?": "", + # "Using distributed or parallel set-up in script?": "", + } + print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the last point.\n") + print(format_dict(info)) + return info + + +def format_dict(d: dict) -> str: + return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" + + +if __name__ == "__main__": + display_sys_info() diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..4721716299a3921beff2ed9bb1ef101d687adbdc --- /dev/null +++ b/lerobot/scripts/eval.py @@ -0,0 +1,506 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Evaluate a policy on an environment by running rollouts and computing metrics. + +Usage examples: + +You want to evaluate a model from the hub (eg: https://huggingface.co/lerobot/diffusion_pusht) +for 10 episodes. + +``` +python lerobot/scripts/eval.py \ + --policy.path=lerobot/diffusion_pusht \ + --env.type=pusht \ + --eval.batch_size=10 \ + --eval.n_episodes=10 \ + --use_amp=false \ + --device=cuda +``` + +OR, you want to evaluate a model checkpoint from the LeRobot training script for 10 episodes. +``` +python lerobot/scripts/eval.py \ + --policy.path=outputs/train/diffusion_pusht/checkpoints/005000/pretrained_model \ + --env.type=pusht \ + --eval.batch_size=10 \ + --eval.n_episodes=10 \ + --use_amp=false \ + --device=cuda +``` + +Note that in both examples, the repo/folder should contain at least `config.json` and `model.safetensors` files. + +You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py +""" + +import json +import logging +import threading +import time +from contextlib import nullcontext +from copy import deepcopy +from dataclasses import asdict +from pathlib import Path +from pprint import pformat +from typing import Callable + +import einops +import gymnasium as gym +import numpy as np +import torch +from termcolor import colored +from torch import Tensor, nn +from tqdm import trange + +from lerobot.common.envs.factory import make_env +from lerobot.common.envs.utils import add_envs_task, check_env_attributes_and_types, preprocess_observation +from lerobot.common.policies.factory import make_policy +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.utils import get_device_from_parameters +from lerobot.common.utils.io_utils import write_video +from lerobot.common.utils.random_utils import set_seed +from lerobot.common.utils.utils import ( + get_safe_torch_device, + init_logging, + inside_slurm, +) +from lerobot.configs import parser +from lerobot.configs.eval import EvalPipelineConfig + + +def rollout( + env: gym.vector.VectorEnv, + policy: PreTrainedPolicy, + seeds: list[int] | None = None, + return_observations: bool = False, + render_callback: Callable[[gym.vector.VectorEnv], None] | None = None, +) -> dict: + """Run a batched policy rollout once through a batch of environments. + + Note that all environments in the batch are run until the last environment is done. This means some + data will probably need to be discarded (for environments that aren't the first one to be done). + + The return dictionary contains: + (optional) "observation": A dictionary of (batch, sequence + 1, *) tensors mapped to observation + keys. NOTE that this has an extra sequence element relative to the other keys in the + dictionary. This is because an extra observation is included for after the environment is + terminated or truncated. + "action": A (batch, sequence, action_dim) tensor of actions applied based on the observations (not + including the last observations). + "reward": A (batch, sequence) tensor of rewards received for applying the actions. + "success": A (batch, sequence) tensor of success conditions (the only time this can be True is upon + environment termination/truncation). + "done": A (batch, sequence) tensor of **cumulative** done conditions. For any given batch element, + the first True is followed by True's all the way till the end. This can be used for masking + extraneous elements from the sequences above. + + Args: + env: The batch of environments. + policy: The policy. Must be a PyTorch nn module. + seeds: The environments are seeded once at the start of the rollout. If provided, this argument + specifies the seeds for each of the environments. + return_observations: Whether to include all observations in the returned rollout data. Observations + are returned optionally because they typically take more memory to cache. Defaults to False. + render_callback: Optional rendering callback to be used after the environments are reset, and after + every step. + Returns: + The dictionary described above. + """ + assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module." + device = get_device_from_parameters(policy) + + # Reset the policy and environments. + policy.reset() + observation, info = env.reset(seed=seeds) + if render_callback is not None: + render_callback(env) + + all_observations = [] + all_actions = [] + all_rewards = [] + all_successes = [] + all_dones = [] + + step = 0 + # Keep track of which environments are done. + done = np.array([False] * env.num_envs) + max_steps = env.call("_max_episode_steps")[0] + progbar = trange( + max_steps, + desc=f"Running rollout with at most {max_steps} steps", + disable=inside_slurm(), # we dont want progress bar when we use slurm, since it clutters the logs + leave=False, + ) + check_env_attributes_and_types(env) + while not np.all(done): + # Numpy array to tensor and changing dictionary keys to LeRobot policy format. + observation = preprocess_observation(observation) + if return_observations: + all_observations.append(deepcopy(observation)) + + observation = { + key: observation[key].to(device, non_blocking=device.type == "cuda") for key in observation + } + + # Infer "task" from attributes of environments. + # TODO: works with SyncVectorEnv but not AsyncVectorEnv + observation = add_envs_task(env, observation) + + with torch.inference_mode(): + action = policy.select_action(observation) + + # Convert to CPU / numpy. + action = action.to("cpu").numpy() + assert action.ndim == 2, "Action dimensions should be (batch, action_dim)" + + # Apply the next action. + observation, reward, terminated, truncated, info = env.step(action) + if render_callback is not None: + render_callback(env) + + # VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't + # available of none of the envs finished. + if "final_info" in info: + successes = [info["is_success"] if info is not None else False for info in info["final_info"]] + else: + successes = [False] * env.num_envs + + # Keep track of which environments are done so far. + done = terminated | truncated | done + + all_actions.append(torch.from_numpy(action)) + all_rewards.append(torch.from_numpy(reward)) + all_dones.append(torch.from_numpy(done)) + all_successes.append(torch.tensor(successes)) + + step += 1 + running_success_rate = ( + einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean() + ) + progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"}) + progbar.update() + + # Track the final observation. + if return_observations: + observation = preprocess_observation(observation) + all_observations.append(deepcopy(observation)) + + # Stack the sequence along the first dimension so that we have (batch, sequence, *) tensors. + ret = { + "action": torch.stack(all_actions, dim=1), + "reward": torch.stack(all_rewards, dim=1), + "success": torch.stack(all_successes, dim=1), + "done": torch.stack(all_dones, dim=1), + } + if return_observations: + stacked_observations = {} + for key in all_observations[0]: + stacked_observations[key] = torch.stack([obs[key] for obs in all_observations], dim=1) + ret["observation"] = stacked_observations + + if hasattr(policy, "use_original_modules"): + policy.use_original_modules() + + return ret + + +def eval_policy( + env: gym.vector.VectorEnv, + policy: PreTrainedPolicy, + n_episodes: int, + max_episodes_rendered: int = 0, + videos_dir: Path | None = None, + return_episode_data: bool = False, + start_seed: int | None = None, +) -> dict: + """ + Args: + env: The batch of environments. + policy: The policy. + n_episodes: The number of episodes to evaluate. + max_episodes_rendered: Maximum number of episodes to render into videos. + videos_dir: Where to save rendered videos. + return_episode_data: Whether to return episode data for online training. Incorporates the data into + the "episodes" key of the returned dictionary. + start_seed: The first seed to use for the first individual rollout. For all subsequent rollouts the + seed is incremented by 1. If not provided, the environments are not manually seeded. + Returns: + Dictionary with metrics and data regarding the rollouts. + """ + if max_episodes_rendered > 0 and not videos_dir: + raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.") + + if not isinstance(policy, PreTrainedPolicy): + raise ValueError( + f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided." + ) + + start = time.time() + policy.eval() + + # Determine how many batched rollouts we need to get n_episodes. Note that if n_episodes is not evenly + # divisible by env.num_envs we end up discarding some data in the last batch. + n_batches = n_episodes // env.num_envs + int((n_episodes % env.num_envs) != 0) + + # Keep track of some metrics. + sum_rewards = [] + max_rewards = [] + all_successes = [] + all_seeds = [] + threads = [] # for video saving threads + n_episodes_rendered = 0 # for saving the correct number of videos + + # Callback for visualization. + def render_frame(env: gym.vector.VectorEnv): + # noqa: B023 + if n_episodes_rendered >= max_episodes_rendered: + return + n_to_render_now = min(max_episodes_rendered - n_episodes_rendered, env.num_envs) + if isinstance(env, gym.vector.SyncVectorEnv): + ep_frames.append(np.stack([env.envs[i].render() for i in range(n_to_render_now)])) # noqa: B023 + elif isinstance(env, gym.vector.AsyncVectorEnv): + # Here we must render all frames and discard any we don't need. + ep_frames.append(np.stack(env.call("render")[:n_to_render_now])) + + if max_episodes_rendered > 0: + video_paths: list[str] = [] + + if return_episode_data: + episode_data: dict | None = None + + # we dont want progress bar when we use slurm, since it clutters the logs + progbar = trange(n_batches, desc="Stepping through eval batches", disable=inside_slurm()) + for batch_ix in progbar: + # Cache frames for rendering videos. Each item will be (b, h, w, c), and the list indexes the rollout + # step. + if max_episodes_rendered > 0: + ep_frames: list[np.ndarray] = [] + + if start_seed is None: + seeds = None + else: + seeds = range( + start_seed + (batch_ix * env.num_envs), start_seed + ((batch_ix + 1) * env.num_envs) + ) + rollout_data = rollout( + env, + policy, + seeds=list(seeds) if seeds else None, + return_observations=return_episode_data, + render_callback=render_frame if max_episodes_rendered > 0 else None, + ) + + # Figure out where in each rollout sequence the first done condition was encountered (results after + # this won't be included). + n_steps = rollout_data["done"].shape[1] + # Note: this relies on a property of argmax: that it returns the first occurrence as a tiebreaker. + done_indices = torch.argmax(rollout_data["done"].to(int), dim=1) + + # Make a mask with shape (batch, n_steps) to mask out rollout data after the first done + # (batch-element-wise). Note the `done_indices + 1` to make sure to keep the data from the done step. + mask = (torch.arange(n_steps) <= einops.repeat(done_indices + 1, "b -> b s", s=n_steps)).int() + # Extend metrics. + batch_sum_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "sum") + sum_rewards.extend(batch_sum_rewards.tolist()) + batch_max_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "max") + max_rewards.extend(batch_max_rewards.tolist()) + batch_successes = einops.reduce((rollout_data["success"] * mask), "b n -> b", "any") + all_successes.extend(batch_successes.tolist()) + if seeds: + all_seeds.extend(seeds) + else: + all_seeds.append(None) + + # FIXME: episode_data is either None or it doesn't exist + if return_episode_data: + this_episode_data = _compile_episode_data( + rollout_data, + done_indices, + start_episode_index=batch_ix * env.num_envs, + start_data_index=(0 if episode_data is None else (episode_data["index"][-1].item() + 1)), + fps=env.unwrapped.metadata["render_fps"], + ) + if episode_data is None: + episode_data = this_episode_data + else: + # Some sanity checks to make sure we are correctly compiling the data. + assert episode_data["episode_index"][-1] + 1 == this_episode_data["episode_index"][0] + assert episode_data["index"][-1] + 1 == this_episode_data["index"][0] + # Concatenate the episode data. + episode_data = {k: torch.cat([episode_data[k], this_episode_data[k]]) for k in episode_data} + + # Maybe render video for visualization. + if max_episodes_rendered > 0 and len(ep_frames) > 0: + batch_stacked_frames = np.stack(ep_frames, axis=1) # (b, t, *) + for stacked_frames, done_index in zip( + batch_stacked_frames, done_indices.flatten().tolist(), strict=False + ): + if n_episodes_rendered >= max_episodes_rendered: + break + + videos_dir.mkdir(parents=True, exist_ok=True) + video_path = videos_dir / f"eval_episode_{n_episodes_rendered}.mp4" + video_paths.append(str(video_path)) + thread = threading.Thread( + target=write_video, + args=( + str(video_path), + stacked_frames[: done_index + 1], # + 1 to capture the last observation + env.unwrapped.metadata["render_fps"], + ), + ) + thread.start() + threads.append(thread) + n_episodes_rendered += 1 + + progbar.set_postfix( + {"running_success_rate": f"{np.mean(all_successes[:n_episodes]).item() * 100:.1f}%"} + ) + + # Wait till all video rendering threads are done. + for thread in threads: + thread.join() + + # Compile eval info. + info = { + "per_episode": [ + { + "episode_ix": i, + "sum_reward": sum_reward, + "max_reward": max_reward, + "success": success, + "seed": seed, + } + for i, (sum_reward, max_reward, success, seed) in enumerate( + zip( + sum_rewards[:n_episodes], + max_rewards[:n_episodes], + all_successes[:n_episodes], + all_seeds[:n_episodes], + strict=True, + ) + ) + ], + "aggregated": { + "avg_sum_reward": float(np.nanmean(sum_rewards[:n_episodes])), + "avg_max_reward": float(np.nanmean(max_rewards[:n_episodes])), + "pc_success": float(np.nanmean(all_successes[:n_episodes]) * 100), + "eval_s": time.time() - start, + "eval_ep_s": (time.time() - start) / n_episodes, + }, + } + + if return_episode_data: + info["episodes"] = episode_data + + if max_episodes_rendered > 0: + info["video_paths"] = video_paths + + return info + + +def _compile_episode_data( + rollout_data: dict, done_indices: Tensor, start_episode_index: int, start_data_index: int, fps: float +) -> dict: + """Convenience function for `eval_policy(return_episode_data=True)` + + Compiles all the rollout data into a Hugging Face dataset. + + Similar logic is implemented when datasets are pushed to hub (see: `push_to_hub`). + """ + ep_dicts = [] + total_frames = 0 + for ep_ix in range(rollout_data["action"].shape[0]): + # + 2 to include the first done frame and the last observation frame. + num_frames = done_indices[ep_ix].item() + 2 + total_frames += num_frames + + # Here we do `num_frames - 1` as we don't want to include the last observation frame just yet. + ep_dict = { + "action": rollout_data["action"][ep_ix, : num_frames - 1], + "episode_index": torch.tensor([start_episode_index + ep_ix] * (num_frames - 1)), + "frame_index": torch.arange(0, num_frames - 1, 1), + "timestamp": torch.arange(0, num_frames - 1, 1) / fps, + "next.done": rollout_data["done"][ep_ix, : num_frames - 1], + "next.success": rollout_data["success"][ep_ix, : num_frames - 1], + "next.reward": rollout_data["reward"][ep_ix, : num_frames - 1].type(torch.float32), + } + + # For the last observation frame, all other keys will just be copy padded. + for k in ep_dict: + ep_dict[k] = torch.cat([ep_dict[k], ep_dict[k][-1:]]) + + for key in rollout_data["observation"]: + ep_dict[key] = rollout_data["observation"][key][ep_ix, :num_frames] + + ep_dicts.append(ep_dict) + + data_dict = {} + for key in ep_dicts[0]: + data_dict[key] = torch.cat([x[key] for x in ep_dicts]) + + data_dict["index"] = torch.arange(start_data_index, start_data_index + total_frames, 1) + + return data_dict + + +@parser.wrap() +def eval_main(cfg: EvalPipelineConfig): + logging.info(pformat(asdict(cfg))) + + # Check device is available + device = get_safe_torch_device(cfg.policy.device, log=True) + + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + set_seed(cfg.seed) + + logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}") + + logging.info("Making environment.") + env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs) + + logging.info("Making policy.") + + policy = make_policy( + cfg=cfg.policy, + env_cfg=cfg.env, + ) + policy.eval() + + with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(): + info = eval_policy( + env, + policy, + cfg.eval.n_episodes, + max_episodes_rendered=10, + videos_dir=Path(cfg.output_dir) / "videos", + start_seed=cfg.seed, + ) + print(info["aggregated"]) + + # Save info + with open(Path(cfg.output_dir) / "eval_info.json", "w") as f: + json.dump(info, f, indent=2) + + env.close() + + logging.info("End of eval") + + +if __name__ == "__main__": + init_logging() + eval_main() diff --git a/lerobot/scripts/find_joint_limits.py b/lerobot/scripts/find_joint_limits.py new file mode 100644 index 0000000000000000000000000000000000000000..856b478010c7fef7c8aab17fa7de4d65f1965c0d --- /dev/null +++ b/lerobot/scripts/find_joint_limits.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Simple script to control a robot from teleoperation. + +Example: + +```shell +python -m lerobot.scripts.server.find_joint_limits \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=black \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue +``` +""" + +import time +from dataclasses import dataclass + +import draccus +import numpy as np + +from lerobot.common.model.kinematics import RobotKinematics +from lerobot.common.robots import ( # noqa: F401 + RobotConfig, + koch_follower, + make_robot_from_config, + so100_follower, +) +from lerobot.common.teleoperators import ( # noqa: F401 + TeleoperatorConfig, + gamepad, + koch_leader, + make_teleoperator_from_config, + so100_leader, +) + + +@dataclass +class FindJointLimitsConfig: + teleop: TeleoperatorConfig + robot: RobotConfig + # Limit the maximum frames per second. By default, no limit. + teleop_time_s: float = 30 + # Display all cameras on screen + display_data: bool = False + + +@draccus.wrap() +def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig): + teleop = make_teleoperator_from_config(cfg.teleop) + robot = make_robot_from_config(cfg.robot) + + teleop.connect() + robot.connect() + + start_episode_t = time.perf_counter() + robot_type = getattr(robot.config, "robot_type", "so101") + if "so100" in robot_type or "so101" in robot_type: + # Note to be compatible with the rest of the codebase, + # we are using the new calibration method for so101 and so100 + robot_type = "so_new_calibration" + kinematics = RobotKinematics(robot_type=robot_type) + + # Initialize min/max values + observation = robot.get_observation() + joint_positions = np.array([observation[f"{key}.pos"] for key in robot.bus.motors]) + ee_pos = kinematics.forward_kinematics(joint_positions, frame="gripper_tip")[:3, 3] + + max_pos = joint_positions.copy() + min_pos = joint_positions.copy() + max_ee = ee_pos.copy() + min_ee = ee_pos.copy() + + while True: + action = teleop.get_action() + robot.send_action(action) + + observation = robot.get_observation() + joint_positions = np.array([observation[f"{key}.pos"] for key in robot.bus.motors]) + ee_pos = kinematics.forward_kinematics(joint_positions, frame="gripper_tip")[:3, 3] + + # Skip initial warmup period + if (time.perf_counter() - start_episode_t) < 5: + continue + + # Update min/max values + max_ee = np.maximum(max_ee, ee_pos) + min_ee = np.minimum(min_ee, ee_pos) + max_pos = np.maximum(max_pos, joint_positions) + min_pos = np.minimum(min_pos, joint_positions) + + if time.perf_counter() - start_episode_t > cfg.teleop_time_s: + print(f"Max ee position {np.round(max_ee, 4).tolist()}") + print(f"Min ee position {np.round(min_ee, 4).tolist()}") + print(f"Max joint pos position {np.round(max_pos, 4).tolist()}") + print(f"Min joint pos position {np.round(min_pos, 4).tolist()}") + break + + +if __name__ == "__main__": + find_joint_and_ee_bounds() diff --git a/lerobot/scripts/push_pretrained.py b/lerobot/scripts/push_pretrained.py new file mode 100644 index 0000000000000000000000000000000000000000..8a813179b52ab474555fbbb5e3225201a467854e --- /dev/null +++ b/lerobot/scripts/push_pretrained.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Once you have trained a policy with our training script (lerobot/scripts/train.py), use this script to push it +to the hub. + +Example: + +```bash +python lerobot/scripts/push_pretrained.py \ + --pretrained_path=outputs/train/act_aloha_sim_transfer_cube_human/checkpoints/last/pretrained_model \ + --repo_id=lerobot/act_aloha_sim_transfer_cube_human +``` +""" + +from dataclasses import dataclass +from pathlib import Path + +import draccus +from huggingface_hub import HfApi + + +@dataclass +class PushPreTrainedConfig: + pretrained_path: Path + repo_id: str + branch: str | None = None + private: bool = False + exist_ok: bool = False + + +@draccus.wrap() +def main(cfg: PushPreTrainedConfig): + hub_api = HfApi() + hub_api.create_repo( + repo_id=cfg.repo_id, + private=cfg.private, + repo_type="model", + exist_ok=cfg.exist_ok, + ) + if cfg.branch: + hub_api.create_branch( + repo_id=cfg.repo_id, + branch=cfg.branch, + repo_type="model", + exist_ok=cfg.exist_ok, + ) + + hub_api.upload_folder( + repo_id=cfg.repo_id, + folder_path=cfg.pretrained_path, + repo_type="model", + revision=cfg.branch, + ) + + +if __name__ == "__main__": + main() diff --git a/lerobot/scripts/rl/actor.py b/lerobot/scripts/rl/actor.py new file mode 100644 index 0000000000000000000000000000000000000000..d0833cd40b4c135d3c90353ffb8c8221e0dec837 --- /dev/null +++ b/lerobot/scripts/rl/actor.py @@ -0,0 +1,709 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Actor server runner for distributed HILSerl robot policy training. + +This script implements the actor component of the distributed HILSerl architecture. +It executes the policy in the robot environment, collects experience, +and sends transitions to the learner server for policy updates. + +Examples of usage: + +- Start an actor server for real robot training with human-in-the-loop intervention: +```bash +python lerobot/scripts/rl/actor.py --config_path lerobot/configs/train_config_hilserl_so100.json +``` + +**NOTE**: The actor server requires a running learner server to connect to. Ensure the learner +server is started before launching the actor. + +**NOTE**: Human intervention is key to HILSerl training. Press the upper right trigger button on the +gamepad to take control of the robot during training. Initially intervene frequently, then gradually +reduce interventions as the policy improves. + +**WORKFLOW**: +1. Determine robot workspace bounds using `find_joint_limits.py` +2. Record demonstrations with `gym_manipulator.py` in record mode +3. Process the dataset and determine camera crops with `crop_dataset_roi.py` +4. Start the learner server with the training configuration +5. Start this actor server with the same configuration +6. Use human interventions to guide policy learning + +For more details on the complete HILSerl training workflow, see: +https://github.com/michel-aractingi/lerobot-hilserl-guide +""" + +import logging +import os +import time +from functools import lru_cache +from queue import Empty + +import grpc +import torch +from torch import nn +from torch.multiprocessing import Event, Queue + +from lerobot.common.cameras import opencv # noqa: F401 +from lerobot.common.policies.factory import make_policy +from lerobot.common.policies.sac.modeling_sac import SACPolicy +from lerobot.common.robots import so100_follower # noqa: F401 +from lerobot.common.teleoperators import gamepad, so101_leader # noqa: F401 +from lerobot.common.transport import services_pb2, services_pb2_grpc +from lerobot.common.transport.utils import ( + bytes_to_state_dict, + python_object_to_bytes, + receive_bytes_in_chunks, + send_bytes_in_chunks, + transitions_to_bytes, +) +from lerobot.common.utils.process import ProcessSignalHandler +from lerobot.common.utils.queue import get_last_item_from_queue +from lerobot.common.utils.random_utils import set_seed +from lerobot.common.utils.robot_utils import busy_wait +from lerobot.common.utils.transition import ( + Transition, + move_state_dict_to_device, + move_transition_to_device, +) +from lerobot.common.utils.utils import ( + TimerManager, + get_safe_torch_device, + init_logging, +) +from lerobot.configs import parser +from lerobot.configs.train import TrainRLServerPipelineConfig +from lerobot.scripts.rl import learner_service +from lerobot.scripts.rl.gym_manipulator import make_robot_env + +ACTOR_SHUTDOWN_TIMEOUT = 30 + + +################################################# +# Main entry point # +################################################# + + +@parser.wrap() +def actor_cli(cfg: TrainRLServerPipelineConfig): + cfg.validate() + display_pid = False + if not use_threads(cfg): + import torch.multiprocessing as mp + + mp.set_start_method("spawn") + display_pid = True + + # Create logs directory to ensure it exists + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"actor_{cfg.job_name}.log") + + # Initialize logging with explicit log file + init_logging(log_file=log_file, display_pid=display_pid) + logging.info(f"Actor logging initialized, writing to {log_file}") + + is_threaded = use_threads(cfg) + shutdown_event = ProcessSignalHandler(is_threaded, display_pid=display_pid).shutdown_event + + learner_client, grpc_channel = learner_service_client( + host=cfg.policy.actor_learner_config.learner_host, + port=cfg.policy.actor_learner_config.learner_port, + ) + + logging.info("[ACTOR] Establishing connection with Learner") + if not establish_learner_connection(learner_client, shutdown_event): + logging.error("[ACTOR] Failed to establish connection with Learner") + return + + if not use_threads(cfg): + # If we use multithreading, we can reuse the channel + grpc_channel.close() + grpc_channel = None + + logging.info("[ACTOR] Connection with Learner established") + + parameters_queue = Queue() + transitions_queue = Queue() + interactions_queue = Queue() + + concurrency_entity = None + if use_threads(cfg): + from threading import Thread + + concurrency_entity = Thread + else: + from multiprocessing import Process + + concurrency_entity = Process + + receive_policy_process = concurrency_entity( + target=receive_policy, + args=(cfg, parameters_queue, shutdown_event, grpc_channel), + daemon=True, + ) + + transitions_process = concurrency_entity( + target=send_transitions, + args=(cfg, transitions_queue, shutdown_event, grpc_channel), + daemon=True, + ) + + interactions_process = concurrency_entity( + target=send_interactions, + args=(cfg, interactions_queue, shutdown_event, grpc_channel), + daemon=True, + ) + + transitions_process.start() + interactions_process.start() + receive_policy_process.start() + + act_with_policy( + cfg=cfg, + shutdown_event=shutdown_event, + parameters_queue=parameters_queue, + transitions_queue=transitions_queue, + interactions_queue=interactions_queue, + ) + logging.info("[ACTOR] Policy process joined") + + logging.info("[ACTOR] Closing queues") + transitions_queue.close() + interactions_queue.close() + parameters_queue.close() + + transitions_process.join() + logging.info("[ACTOR] Transitions process joined") + interactions_process.join() + logging.info("[ACTOR] Interactions process joined") + receive_policy_process.join() + logging.info("[ACTOR] Receive policy process joined") + + logging.info("[ACTOR] join queues") + transitions_queue.cancel_join_thread() + interactions_queue.cancel_join_thread() + parameters_queue.cancel_join_thread() + + logging.info("[ACTOR] queues closed") + + +################################################# +# Core algorithm functions # +################################################# + + +def act_with_policy( + cfg: TrainRLServerPipelineConfig, + shutdown_event: any, # Event, + parameters_queue: Queue, + transitions_queue: Queue, + interactions_queue: Queue, +): + """ + Executes policy interaction within the environment. + + This function rolls out the policy in the environment, collecting interaction data and pushing it to a queue for streaming to the learner. + Once an episode is completed, updated network parameters received from the learner are retrieved from a queue and loaded into the network. + + Args: + cfg: Configuration settings for the interaction process. + shutdown_event: Event to check if the process should shutdown. + parameters_queue: Queue to receive updated network parameters from the learner. + transitions_queue: Queue to send transitions to the learner. + interactions_queue: Queue to send interactions to the learner. + """ + # Initialize logging for multiprocessing + if not use_threads(cfg): + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"actor_policy_{os.getpid()}.log") + init_logging(log_file=log_file, display_pid=True) + logging.info("Actor policy process logging initialized") + + logging.info("make_env online") + + online_env = make_robot_env(cfg=cfg.env) + + set_seed(cfg.seed) + device = get_safe_torch_device(cfg.policy.device, log=True) + + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + + logging.info("make_policy") + + ### Instantiate the policy in both the actor and learner processes + ### To avoid sending a SACPolicy object through the port, we create a policy instance + ### on both sides, the learner sends the updated parameters every n steps to update the actor's parameters + policy: SACPolicy = make_policy( + cfg=cfg.policy, + env_cfg=cfg.env, + ) + policy = policy.eval() + assert isinstance(policy, nn.Module) + + obs, info = online_env.reset() + + # NOTE: For the moment we will solely handle the case of a single environment + sum_reward_episode = 0 + list_transition_to_send_to_learner = [] + episode_intervention = False + # Add counters for intervention rate calculation + episode_intervention_steps = 0 + episode_total_steps = 0 + + policy_timer = TimerManager("Policy inference", log=False) + + for interaction_step in range(cfg.policy.online_steps): + start_time = time.perf_counter() + if shutdown_event.is_set(): + logging.info("[ACTOR] Shutting down act_with_policy") + return + + if interaction_step >= cfg.policy.online_step_before_learning: + # Time policy inference and check if it meets FPS requirement + with policy_timer: + action = policy.select_action(batch=obs) + policy_fps = policy_timer.fps_last + + log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step) + + else: + action = online_env.action_space.sample() + + next_obs, reward, done, truncated, info = online_env.step(action) + + sum_reward_episode += float(reward) + # Increment total steps counter for intervention rate + episode_total_steps += 1 + + # NOTE: We override the action if the intervention is True, because the action applied is the intervention action + if "is_intervention" in info and info["is_intervention"]: + # NOTE: The action space for demonstration before hand is with the full action space + # but sometimes for example we want to deactivate the gripper + action = info["action_intervention"] + episode_intervention = True + # Increment intervention steps counter + episode_intervention_steps += 1 + + list_transition_to_send_to_learner.append( + Transition( + state=obs, + action=action, + reward=reward, + next_state=next_obs, + done=done, + truncated=truncated, # TODO: (azouitine) Handle truncation properly + complementary_info=info, + ) + ) + # assign obs to the next obs and continue the rollout + obs = next_obs + + if done or truncated: + logging.info(f"[ACTOR] Global step {interaction_step}: Episode reward: {sum_reward_episode}") + + update_policy_parameters(policy=policy.actor, parameters_queue=parameters_queue, device=device) + + if len(list_transition_to_send_to_learner) > 0: + push_transitions_to_transport_queue( + transitions=list_transition_to_send_to_learner, + transitions_queue=transitions_queue, + ) + list_transition_to_send_to_learner = [] + + stats = get_frequency_stats(policy_timer) + policy_timer.reset() + + # Calculate intervention rate + intervention_rate = 0.0 + if episode_total_steps > 0: + intervention_rate = episode_intervention_steps / episode_total_steps + + # Send episodic reward to the learner + interactions_queue.put( + python_object_to_bytes( + { + "Episodic reward": sum_reward_episode, + "Interaction step": interaction_step, + "Episode intervention": int(episode_intervention), + "Intervention rate": intervention_rate, + **stats, + } + ) + ) + + # Reset intervention counters + sum_reward_episode = 0.0 + episode_intervention = False + episode_intervention_steps = 0 + episode_total_steps = 0 + obs, info = online_env.reset() + + if cfg.env.fps is not None: + dt_time = time.perf_counter() - start_time + busy_wait(1 / cfg.env.fps - dt_time) + + +################################################# +# Communication Functions - Group all gRPC/messaging functions # +################################################# + + +def establish_learner_connection( + stub: services_pb2_grpc.LearnerServiceStub, + shutdown_event: Event, # type: ignore + attempts: int = 30, +): + """Establish a connection with the learner. + + Args: + stub (services_pb2_grpc.LearnerServiceStub): The stub to use for the connection. + shutdown_event (Event): The event to check if the connection should be established. + attempts (int): The number of attempts to establish the connection. + Returns: + bool: True if the connection is established, False otherwise. + """ + for _ in range(attempts): + if shutdown_event.is_set(): + logging.info("[ACTOR] Shutting down establish_learner_connection") + return False + + # Force a connection attempt and check state + try: + logging.info("[ACTOR] Send ready message to Learner") + if stub.Ready(services_pb2.Empty()) == services_pb2.Empty(): + return True + except grpc.RpcError as e: + logging.error(f"[ACTOR] Waiting for Learner to be ready... {e}") + time.sleep(2) + return False + + +@lru_cache(maxsize=1) +def learner_service_client( + host: str = "127.0.0.1", + port: int = 50051, +) -> tuple[services_pb2_grpc.LearnerServiceStub, grpc.Channel]: + import json + + """ + Returns a client for the learner service. + + GRPC uses HTTP/2, which is a binary protocol and multiplexes requests over a single connection. + So we need to create only one client and reuse it. + """ + + service_config = { + "methodConfig": [ + { + "name": [{}], # Applies to ALL methods in ALL services + "retryPolicy": { + "maxAttempts": 5, # Max retries (total attempts = 5) + "initialBackoff": "0.1s", # First retry after 0.1s + "maxBackoff": "2s", # Max wait time between retries + "backoffMultiplier": 2, # Exponential backoff factor + "retryableStatusCodes": [ + "UNAVAILABLE", + "DEADLINE_EXCEEDED", + ], # Retries on network failures + }, + } + ] + } + + service_config_json = json.dumps(service_config) + + channel = grpc.insecure_channel( + f"{host}:{port}", + options=[ + ("grpc.max_receive_message_length", learner_service.MAX_MESSAGE_SIZE), + ("grpc.max_send_message_length", learner_service.MAX_MESSAGE_SIZE), + ("grpc.enable_retries", 1), + ("grpc.service_config", service_config_json), + ], + ) + stub = services_pb2_grpc.LearnerServiceStub(channel) + logging.info("[ACTOR] Learner service client created") + return stub, channel + + +def receive_policy( + cfg: TrainRLServerPipelineConfig, + parameters_queue: Queue, + shutdown_event: Event, # type: ignore + learner_client: services_pb2_grpc.LearnerServiceStub | None = None, + grpc_channel: grpc.Channel | None = None, +): + """Receive parameters from the learner. + + Args: + cfg (TrainRLServerPipelineConfig): The configuration for the actor. + parameters_queue (Queue): The queue to receive the parameters. + shutdown_event (Event): The event to check if the process should shutdown. + """ + logging.info("[ACTOR] Start receiving parameters from the Learner") + if not use_threads(cfg): + # Create a process-specific log file + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"actor_receive_policy_{os.getpid()}.log") + + # Initialize logging with explicit log file + init_logging(log_file=log_file, display_pid=True) + logging.info("Actor receive policy process logging initialized") + + # Setup process handlers to handle shutdown signal + # But use shutdown event from the main process + _ = ProcessSignalHandler(use_threads=False, display_pid=True) + + if grpc_channel is None or learner_client is None: + learner_client, grpc_channel = learner_service_client( + host=cfg.policy.actor_learner_config.learner_host, + port=cfg.policy.actor_learner_config.learner_port, + ) + + try: + iterator = learner_client.StreamParameters(services_pb2.Empty()) + receive_bytes_in_chunks( + iterator, + parameters_queue, + shutdown_event, + log_prefix="[ACTOR] parameters", + ) + + except grpc.RpcError as e: + logging.error(f"[ACTOR] gRPC error: {e}") + + if not use_threads(cfg): + grpc_channel.close() + logging.info("[ACTOR] Received policy loop stopped") + + +def send_transitions( + cfg: TrainRLServerPipelineConfig, + transitions_queue: Queue, + shutdown_event: any, # Event, + learner_client: services_pb2_grpc.LearnerServiceStub | None = None, + grpc_channel: grpc.Channel | None = None, +) -> services_pb2.Empty: + """ + Sends transitions to the learner. + + This function continuously retrieves messages from the queue and processes: + + - Transition Data: + - A batch of transitions (observation, action, reward, next observation) is collected. + - Transitions are moved to the CPU and serialized using PyTorch. + - The serialized data is wrapped in a `services_pb2.Transition` message and sent to the learner. + """ + + if not use_threads(cfg): + # Create a process-specific log file + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"actor_transitions_{os.getpid()}.log") + + # Initialize logging with explicit log file + init_logging(log_file=log_file, display_pid=True) + logging.info("Actor transitions process logging initialized") + + if grpc_channel is None or learner_client is None: + learner_client, grpc_channel = learner_service_client( + host=cfg.policy.actor_learner_config.learner_host, + port=cfg.policy.actor_learner_config.learner_port, + ) + + try: + learner_client.SendTransitions( + transitions_stream( + shutdown_event, transitions_queue, cfg.policy.actor_learner_config.queue_get_timeout + ) + ) + except grpc.RpcError as e: + logging.error(f"[ACTOR] gRPC error: {e}") + + logging.info("[ACTOR] Finished streaming transitions") + + if not use_threads(cfg): + grpc_channel.close() + logging.info("[ACTOR] Transitions process stopped") + + +def send_interactions( + cfg: TrainRLServerPipelineConfig, + interactions_queue: Queue, + shutdown_event: Event, # type: ignore + learner_client: services_pb2_grpc.LearnerServiceStub | None = None, + grpc_channel: grpc.Channel | None = None, +) -> services_pb2.Empty: + """ + Sends interactions to the learner. + + This function continuously retrieves messages from the queue and processes: + + - Interaction Messages: + - Contains useful statistics about episodic rewards and policy timings. + - The message is serialized using `pickle` and sent to the learner. + """ + + if not use_threads(cfg): + # Create a process-specific log file + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"actor_interactions_{os.getpid()}.log") + + # Initialize logging with explicit log file + init_logging(log_file=log_file, display_pid=True) + logging.info("Actor interactions process logging initialized") + + # Setup process handlers to handle shutdown signal + # But use shutdown event from the main process + _ = ProcessSignalHandler(use_threads=False, display_pid=True) + + if grpc_channel is None or learner_client is None: + learner_client, grpc_channel = learner_service_client( + host=cfg.policy.actor_learner_config.learner_host, + port=cfg.policy.actor_learner_config.learner_port, + ) + + try: + learner_client.SendInteractions( + interactions_stream( + shutdown_event, interactions_queue, cfg.policy.actor_learner_config.queue_get_timeout + ) + ) + except grpc.RpcError as e: + logging.error(f"[ACTOR] gRPC error: {e}") + + logging.info("[ACTOR] Finished streaming interactions") + + if not use_threads(cfg): + grpc_channel.close() + logging.info("[ACTOR] Interactions process stopped") + + +def transitions_stream(shutdown_event: Event, transitions_queue: Queue, timeout: float) -> services_pb2.Empty: # type: ignore + while not shutdown_event.is_set(): + try: + message = transitions_queue.get(block=True, timeout=timeout) + except Empty: + logging.debug("[ACTOR] Transition queue is empty") + continue + + yield from send_bytes_in_chunks( + message, services_pb2.Transition, log_prefix="[ACTOR] Send transitions" + ) + + return services_pb2.Empty() + + +def interactions_stream( + shutdown_event: Event, + interactions_queue: Queue, + timeout: float, # type: ignore +) -> services_pb2.Empty: + while not shutdown_event.is_set(): + try: + message = interactions_queue.get(block=True, timeout=timeout) + except Empty: + logging.debug("[ACTOR] Interaction queue is empty") + continue + + yield from send_bytes_in_chunks( + message, + services_pb2.InteractionMessage, + log_prefix="[ACTOR] Send interactions", + ) + + return services_pb2.Empty() + + +################################################# +# Policy functions # +################################################# + + +def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device): + bytes_state_dict = get_last_item_from_queue(parameters_queue, block=False) + if bytes_state_dict is not None: + logging.info("[ACTOR] Load new parameters from Learner.") + state_dict = bytes_to_state_dict(bytes_state_dict) + state_dict = move_state_dict_to_device(state_dict, device=device) + policy.load_state_dict(state_dict) + + +################################################# +# Utilities functions # +################################################# + + +def push_transitions_to_transport_queue(transitions: list, transitions_queue): + """Send transitions to learner in smaller chunks to avoid network issues. + + Args: + transitions: List of transitions to send + message_queue: Queue to send messages to learner + chunk_size: Size of each chunk to send + """ + transition_to_send_to_learner = [] + for transition in transitions: + tr = move_transition_to_device(transition=transition, device="cpu") + for key, value in tr["state"].items(): + if torch.isnan(value).any(): + logging.warning(f"Found NaN values in transition {key}") + + transition_to_send_to_learner.append(tr) + + transitions_queue.put(transitions_to_bytes(transition_to_send_to_learner)) + + +def get_frequency_stats(timer: TimerManager) -> dict[str, float]: + """Get the frequency statistics of the policy. + + Args: + timer (TimerManager): The timer with collected metrics. + + Returns: + dict[str, float]: The frequency statistics of the policy. + """ + stats = {} + if timer.count > 1: + avg_fps = timer.fps_avg + p90_fps = timer.fps_percentile(90) + logging.debug(f"[ACTOR] Average policy frame rate: {avg_fps}") + logging.debug(f"[ACTOR] Policy frame rate 90th percentile: {p90_fps}") + stats = { + "Policy frequency [Hz]": avg_fps, + "Policy frequency 90th-p [Hz]": p90_fps, + } + return stats + + +def log_policy_frequency_issue(policy_fps: float, cfg: TrainRLServerPipelineConfig, interaction_step: int): + if policy_fps < cfg.env.fps: + logging.warning( + f"[ACTOR] Policy FPS {policy_fps:.1f} below required {cfg.env.fps} at step {interaction_step}" + ) + + +def use_threads(cfg: TrainRLServerPipelineConfig) -> bool: + return cfg.policy.concurrency.actor == "threads" + + +if __name__ == "__main__": + actor_cli() diff --git a/lerobot/scripts/rl/crop_dataset_roi.py b/lerobot/scripts/rl/crop_dataset_roi.py new file mode 100644 index 0000000000000000000000000000000000000000..490f2fe3c4fb2f42602794d890c6f49ac095035e --- /dev/null +++ b/lerobot/scripts/rl/crop_dataset_roi.py @@ -0,0 +1,314 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +from copy import deepcopy +from pathlib import Path +from typing import Dict, Tuple + +import cv2 + +# import torch.nn.functional as F # noqa: N812 +import torchvision.transforms.functional as F # type: ignore # noqa: N812 +from tqdm import tqdm # type: ignore + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + + +def select_rect_roi(img): + """ + Allows the user to draw a rectangular ROI on the image. + + The user must click and drag to draw the rectangle. + - While dragging, the rectangle is dynamically drawn. + - On mouse button release, the rectangle is fixed. + - Press 'c' to confirm the selection. + - Press 'r' to reset the selection. + - Press ESC to cancel. + + Returns: + A tuple (top, left, height, width) representing the rectangular ROI, + or None if no valid ROI is selected. + """ + # Create a working copy of the image + clone = img.copy() + working_img = clone.copy() + + roi = None # Will store the final ROI as (top, left, height, width) + drawing = False + index_x, index_y = -1, -1 # Initial click coordinates + + def mouse_callback(event, x, y, flags, param): + nonlocal index_x, index_y, drawing, roi, working_img + + if event == cv2.EVENT_LBUTTONDOWN: + # Start drawing: record starting coordinates + drawing = True + index_x, index_y = x, y + + elif event == cv2.EVENT_MOUSEMOVE: + if drawing: + # Compute the top-left and bottom-right corners regardless of drag direction + top = min(index_y, y) + left = min(index_x, x) + bottom = max(index_y, y) + right = max(index_x, x) + # Show a temporary image with the current rectangle drawn + temp = working_img.copy() + cv2.rectangle(temp, (left, top), (right, bottom), (0, 255, 0), 2) + cv2.imshow("Select ROI", temp) + + elif event == cv2.EVENT_LBUTTONUP: + # Finish drawing + drawing = False + top = min(index_y, y) + left = min(index_x, x) + bottom = max(index_y, y) + right = max(index_x, x) + height = bottom - top + width = right - left + roi = (top, left, height, width) # (top, left, height, width) + # Draw the final rectangle on the working image and display it + working_img = clone.copy() + cv2.rectangle(working_img, (left, top), (right, bottom), (0, 255, 0), 2) + cv2.imshow("Select ROI", working_img) + + # Create the window and set the callback + cv2.namedWindow("Select ROI") + cv2.setMouseCallback("Select ROI", mouse_callback) + cv2.imshow("Select ROI", working_img) + + print("Instructions for ROI selection:") + print(" - Click and drag to draw a rectangular ROI.") + print(" - Press 'c' to confirm the selection.") + print(" - Press 'r' to reset and draw again.") + print(" - Press ESC to cancel the selection.") + + # Wait until the user confirms with 'c', resets with 'r', or cancels with ESC + while True: + key = cv2.waitKey(1) & 0xFF + # Confirm ROI if one has been drawn + if key == ord("c") and roi is not None: + break + # Reset: clear the ROI and restore the original image + elif key == ord("r"): + working_img = clone.copy() + roi = None + cv2.imshow("Select ROI", working_img) + # Cancel selection for this image + elif key == 27: # ESC key + roi = None + break + + cv2.destroyWindow("Select ROI") + return roi + + +def select_square_roi_for_images(images: dict) -> dict: + """ + For each image in the provided dictionary, open a window to allow the user + to select a rectangular ROI. Returns a dictionary mapping each key to a tuple + (top, left, height, width) representing the ROI. + + Parameters: + images (dict): Dictionary where keys are identifiers and values are OpenCV images. + + Returns: + dict: Mapping of image keys to the selected rectangular ROI. + """ + selected_rois = {} + + for key, img in images.items(): + if img is None: + print(f"Image for key '{key}' is None, skipping.") + continue + + print(f"\nSelect rectangular ROI for image with key: '{key}'") + roi = select_rect_roi(img) + + if roi is None: + print(f"No valid ROI selected for '{key}'.") + else: + selected_rois[key] = roi + print(f"ROI for '{key}': {roi}") + + return selected_rois + + +def get_image_from_lerobot_dataset(dataset: LeRobotDataset): + """ + Find the first row in the dataset and extract the image in order to be used for the crop. + """ + row = dataset[0] + image_dict = {} + for k in row: + if "image" in k: + image_dict[k] = deepcopy(row[k]) + return image_dict + + +def convert_lerobot_dataset_to_cropper_lerobot_dataset( + original_dataset: LeRobotDataset, + crop_params_dict: Dict[str, Tuple[int, int, int, int]], + new_repo_id: str, + new_dataset_root: str, + resize_size: Tuple[int, int] = (128, 128), + push_to_hub: bool = False, + task: str = "", +) -> LeRobotDataset: + """ + Converts an existing LeRobotDataset by iterating over its episodes and frames, + applying cropping and resizing to image observations, and saving a new dataset + with the transformed data. + + Args: + original_dataset (LeRobotDataset): The source dataset. + crop_params_dict (Dict[str, Tuple[int, int, int, int]]): + A dictionary mapping observation keys to crop parameters (top, left, height, width). + new_repo_id (str): Repository id for the new dataset. + new_dataset_root (str): The root directory where the new dataset will be written. + resize_size (Tuple[int, int], optional): The target size (height, width) after cropping. + Defaults to (128, 128). + + Returns: + LeRobotDataset: A new LeRobotDataset where the specified image observations have been cropped + and resized. + """ + # 1. Create a new (empty) LeRobotDataset for writing. + new_dataset = LeRobotDataset.create( + repo_id=new_repo_id, + fps=original_dataset.fps, + root=new_dataset_root, + robot_type=original_dataset.meta.robot_type, + features=original_dataset.meta.info["features"], + use_videos=len(original_dataset.meta.video_keys) > 0, + ) + + # Update the metadata for every image key that will be cropped: + # (Here we simply set the shape to be the final resize_size.) + for key in crop_params_dict: + if key in new_dataset.meta.info["features"]: + new_dataset.meta.info["features"][key]["shape"] = [3] + list(resize_size) + + # TODO: Directly modify the mp4 video + meta info features, instead of recreating a dataset + prev_episode_index = 0 + for frame_idx in tqdm(range(len(original_dataset))): + frame = original_dataset[frame_idx] + + # Create a copy of the frame to add to the new dataset + new_frame = {} + for key, value in frame.items(): + if key in ("task_index", "timestamp", "episode_index", "frame_index", "index", "task"): + continue + if key in ("next.done", "next.reward"): + # if not isinstance(value, str) and len(value.shape) == 0: + value = value.unsqueeze(0) + + if key in crop_params_dict: + top, left, height, width = crop_params_dict[key] + # Apply crop then resize. + cropped = F.crop(value, top, left, height, width) + value = F.resize(cropped, resize_size) + value = value.clamp(0, 1) + + new_frame[key] = value + + new_dataset.add_frame(new_frame, task=task) + + if frame["episode_index"].item() != prev_episode_index: + # Save the episode + new_dataset.save_episode() + prev_episode_index = frame["episode_index"].item() + + # Save the last episode + new_dataset.save_episode() + + if push_to_hub: + new_dataset.push_to_hub() + + return new_dataset + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Crop rectangular ROIs from a LeRobot dataset.") + parser.add_argument( + "--repo-id", + type=str, + default="lerobot", + help="The repository id of the LeRobot dataset to process.", + ) + parser.add_argument( + "--root", + type=str, + default=None, + help="The root directory of the LeRobot dataset.", + ) + parser.add_argument( + "--crop-params-path", + type=str, + default=None, + help="The path to the JSON file containing the ROIs.", + ) + parser.add_argument( + "--push-to-hub", + type=bool, + default=False, + help="Whether to push the new dataset to the hub.", + ) + parser.add_argument( + "--task", + type=str, + default="", + help="The natural language task to describe the dataset.", + ) + args = parser.parse_args() + + dataset = LeRobotDataset(repo_id=args.repo_id, root=args.root) + + images = get_image_from_lerobot_dataset(dataset) + images = {k: v.cpu().permute(1, 2, 0).numpy() for k, v in images.items()} + images = {k: (v * 255).astype("uint8") for k, v in images.items()} + + if args.crop_params_path is None: + rois = select_square_roi_for_images(images) + else: + with open(args.crop_params_path) as f: + rois = json.load(f) + + # Print the selected rectangular ROIs + print("\nSelected Rectangular Regions of Interest (top, left, height, width):") + for key, roi in rois.items(): + print(f"{key}: {roi}") + + new_repo_id = args.repo_id + "_cropped_resized" + new_dataset_root = Path(str(dataset.root) + "_cropped_resized") + + cropped_resized_dataset = convert_lerobot_dataset_to_cropper_lerobot_dataset( + original_dataset=dataset, + crop_params_dict=rois, + new_repo_id=new_repo_id, + new_dataset_root=new_dataset_root, + resize_size=(128, 128), + push_to_hub=args.push_to_hub, + task=args.task, + ) + + meta_dir = new_dataset_root / "meta" + meta_dir.mkdir(exist_ok=True) + + with open(meta_dir / "crop_params.json", "w") as f: + json.dump(rois, f, indent=4) diff --git a/lerobot/scripts/rl/eval_policy.py b/lerobot/scripts/rl/eval_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..c67352b311e4721275adf2abba01d496d0edcdc1 --- /dev/null +++ b/lerobot/scripts/rl/eval_policy.py @@ -0,0 +1,74 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging + +from lerobot.common.cameras import opencv # noqa: F401 +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.policies.factory import make_policy +from lerobot.common.robots import ( # noqa: F401 + RobotConfig, + make_robot_from_config, + so100_follower, +) +from lerobot.common.teleoperators import ( + gamepad, # noqa: F401 + so101_leader, # noqa: F401 +) +from lerobot.configs import parser +from lerobot.configs.train import TrainRLServerPipelineConfig +from lerobot.scripts.rl.gym_manipulator import make_robot_env + +logging.basicConfig(level=logging.INFO) + + +def eval_policy(env, policy, n_episodes): + sum_reward_episode = [] + for _ in range(n_episodes): + obs, _ = env.reset() + episode_reward = 0.0 + while True: + action = policy.select_action(obs) + obs, reward, terminated, truncated, _ = env.step(action) + episode_reward += reward + if terminated or truncated: + break + sum_reward_episode.append(episode_reward) + + logging.info(f"Success after 20 steps {sum_reward_episode}") + logging.info(f"success rate {sum(sum_reward_episode) / len(sum_reward_episode)}") + + +@parser.wrap() +def main(cfg: TrainRLServerPipelineConfig): + env_cfg = cfg.env + env = make_robot_env(env_cfg) + dataset_cfg = cfg.dataset + dataset = LeRobotDataset(repo_id=dataset_cfg.repo_id) + dataset_meta = dataset.meta + + policy = make_policy( + cfg=cfg.policy, + # env_cfg=cfg.env, + ds_meta=dataset_meta, + ) + policy.from_pretrained(env_cfg.pretrained_policy_name_or_path) + policy.eval() + + eval_policy(env, policy=policy, n_episodes=10) + + +if __name__ == "__main__": + main() diff --git a/lerobot/scripts/rl/gym_manipulator.py b/lerobot/scripts/rl/gym_manipulator.py new file mode 100644 index 0000000000000000000000000000000000000000..3210034e9e8fe9eb30e9a83b84c5c23d653341ba --- /dev/null +++ b/lerobot/scripts/rl/gym_manipulator.py @@ -0,0 +1,2171 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +Robot Environment for LeRobot Manipulation Tasks + +This module provides a comprehensive gym-compatible environment for robot manipulation +with support for: +- Multiple robot types (SO100, SO101, Koch and Moss) +- Human intervention via leader-follower control or gamepad + +- End-effector and joint space control +- Image processing (cropping and resizing) + +The environment is built using a composable wrapper pattern where each wrapper +adds specific functionality to the base RobotEnv. + +Example: + env = make_robot_env(cfg) + obs, info = env.reset() + action = policy.select_action(obs) + obs, reward, terminated, truncated, info = env.step(action) +""" + +import logging +import time +from collections import deque +from threading import Lock +from typing import Annotated, Any, Sequence + +import gymnasium as gym +import numpy as np +import torch +import torchvision.transforms.functional as F # noqa: N812 + +from lerobot.common.cameras import opencv # noqa: F401 +from lerobot.common.envs.configs import EnvConfig +from lerobot.common.envs.utils import preprocess_observation +from lerobot.common.model.kinematics import RobotKinematics +from lerobot.common.robots import ( # noqa: F401 + RobotConfig, + make_robot_from_config, + so100_follower, +) +from lerobot.common.teleoperators import ( + gamepad, # noqa: F401 + make_teleoperator_from_config, + so101_leader, # noqa: F401 +) +from lerobot.common.teleoperators.gamepad.teleop_gamepad import GamepadTeleop +from lerobot.common.utils.robot_utils import busy_wait +from lerobot.common.utils.utils import log_say +from lerobot.configs import parser + +logging.basicConfig(level=logging.INFO) + + +def reset_follower_position(robot_arm, target_position): + current_position_dict = robot_arm.bus.sync_read("Present_Position") + current_position = np.array( + [current_position_dict[name] for name in current_position_dict], dtype=np.float32 + ) + trajectory = torch.from_numpy( + np.linspace(current_position, target_position, 50) + ) # NOTE: 30 is just an arbitrary number + for pose in trajectory: + action_dict = dict(zip(current_position_dict, pose, strict=False)) + robot_arm.bus.sync_write("Goal_Position", action_dict) + busy_wait(0.015) + + +class TorchBox(gym.spaces.Box): + """ + A version of gym.spaces.Box that handles PyTorch tensors. + + This class extends gym.spaces.Box to work with PyTorch tensors, + providing compatibility between NumPy arrays and PyTorch tensors. + """ + + def __init__( + self, + low: float | Sequence[float] | np.ndarray, + high: float | Sequence[float] | np.ndarray, + shape: Sequence[int] | None = None, + np_dtype: np.dtype | type = np.float32, + torch_dtype: torch.dtype = torch.float32, + device: str = "cpu", + seed: int | np.random.Generator | None = None, + ) -> None: + """ + Initialize the PyTorch-compatible Box space. + + Args: + low: Lower bounds of the space. + high: Upper bounds of the space. + shape: Shape of the space. If None, inferred from low and high. + np_dtype: NumPy data type for internal storage. + torch_dtype: PyTorch data type for tensor conversion. + device: PyTorch device for returned tensors. + seed: Random seed for sampling. + """ + super().__init__(low, high, shape=shape, dtype=np_dtype, seed=seed) + self.torch_dtype = torch_dtype + self.device = device + + def sample(self) -> torch.Tensor: + """ + Sample a random point from the space. + + Returns: + A PyTorch tensor within the space bounds. + """ + arr = super().sample() + return torch.as_tensor(arr, dtype=self.torch_dtype, device=self.device) + + def contains(self, x: torch.Tensor) -> bool: + """ + Check if a tensor is within the space bounds. + + Args: + x: The PyTorch tensor to check. + + Returns: + Boolean indicating whether the tensor is within bounds. + """ + # Move to CPU/numpy and cast to the internal dtype + arr = x.detach().cpu().numpy().astype(self.dtype, copy=False) + return super().contains(arr) + + def seed(self, seed: int | np.random.Generator | None = None): + """ + Set the random seed for sampling. + + Args: + seed: The random seed to use. + + Returns: + List containing the seed. + """ + super().seed(seed) + return [seed] + + def __repr__(self) -> str: + """ + Return a string representation of the space. + + Returns: + Formatted string with space details. + """ + return ( + f"TorchBox({self.low_repr}, {self.high_repr}, {self.shape}, " + f"np={self.dtype.name}, torch={self.torch_dtype}, device={self.device})" + ) + + +class TorchActionWrapper(gym.Wrapper): + """ + Wrapper that changes the action space to use PyTorch tensors. + + This wrapper modifies the action space to return PyTorch tensors when sampled + and handles converting PyTorch actions to NumPy when stepping the environment. + """ + + def __init__(self, env: gym.Env, device: str): + """ + Initialize the PyTorch action space wrapper. + + Args: + env: The environment to wrap. + device: The PyTorch device to use for tensor operations. + """ + super().__init__(env) + self.action_space = TorchBox( + low=env.action_space.low, + high=env.action_space.high, + shape=env.action_space.shape, + torch_dtype=torch.float32, + device=torch.device("cpu"), + ) + + def step(self, action: torch.Tensor): + """ + Step the environment with a PyTorch tensor action. + + This method handles conversion from PyTorch tensors to NumPy arrays + for compatibility with the underlying environment. + + Args: + action: PyTorch tensor action to take. + + Returns: + Tuple of (observation, reward, terminated, truncated, info). + """ + if action.dim() == 2: + action = action.squeeze(0) + action = action.detach().cpu().numpy() + return self.env.step(action) + + +class RobotEnv(gym.Env): + """ + Gym-compatible environment for evaluating robotic control policies with integrated human intervention. + + This environment wraps a robot interface to provide a consistent API for policy evaluation. It supports both relative (delta) + and absolute joint position commands and automatically configures its observation and action spaces based on the robot's + sensors and configuration. + """ + + def __init__( + self, + robot, + use_gripper: bool = False, + display_cameras: bool = False, + ): + """ + Initialize the RobotEnv environment. + + The environment is set up with a robot interface, which is used to capture observations and send joint commands. The setup + supports both relative (delta) adjustments and absolute joint positions for controlling the robot. + + Args: + robot: The robot interface object used to connect and interact with the physical robot. + display_cameras: If True, the robot's camera feeds will be displayed during execution. + """ + super().__init__() + + self.robot = robot + self.display_cameras = display_cameras + + # Connect to the robot if not already connected. + if not self.robot.is_connected: + self.robot.connect() + + # Episode tracking. + self.current_step = 0 + self.episode_data = None + + self._joint_names = [f"{key}.pos" for key in self.robot.bus.motors] + self._image_keys = self.robot.cameras.keys() + + # Read initial joint positions using the bus + self.current_joint_positions = self._get_observation()["agent_pos"] + + self.use_gripper = use_gripper + + self._setup_spaces() + + def _get_observation(self) -> np.ndarray: + """Helper to convert a dictionary from bus.sync_read to an ordered numpy array.""" + obs_dict = self.robot.get_observation() + joint_positions = np.array([obs_dict[name] for name in self._joint_names], dtype=np.float32) + + images = {key: obs_dict[key] for key in self._image_keys} + return {"agent_pos": joint_positions, "pixels": images} + + def _setup_spaces(self): + """ + Dynamically configure the observation and action spaces based on the robot's capabilities. + + Observation Space: + - For keys with "image": A Box space with pixel values ranging from 0 to 255. + - For non-image keys: A nested Dict space is created under 'observation.state' with a suitable range. + + Action Space: + - The action space is defined as a Box space representing joint position commands. It is defined as relative (delta) + or absolute, based on the configuration. + """ + example_obs = self._get_observation() + + observation_spaces = {} + + # Define observation spaces for images and other states. + if "pixels" in example_obs: + prefix = "observation.images" if len(example_obs["pixels"]) > 1 else "observation.image" + observation_spaces = { + f"{prefix}.{key}": gym.spaces.Box( + low=0, high=255, shape=example_obs["pixels"][key].shape, dtype=np.uint8 + ) + for key in example_obs["pixels"] + } + + observation_spaces["observation.state"] = gym.spaces.Box( + low=0, + high=10, + shape=example_obs["agent_pos"].shape, + dtype=np.float32, + ) + + self.observation_space = gym.spaces.Dict(observation_spaces) + + # Define the action space for joint positions along with setting an intervention flag. + action_dim = 3 + bounds = {} + bounds["min"] = -np.ones(action_dim) + bounds["max"] = np.ones(action_dim) + + if self.use_gripper: + action_dim += 1 + bounds["min"] = np.concatenate([bounds["min"], [0]]) + bounds["max"] = np.concatenate([bounds["max"], [2]]) + + self.action_space = gym.spaces.Box( + low=bounds["min"], + high=bounds["max"], + shape=(action_dim,), + dtype=np.float32, + ) + + def reset(self, seed=None, options=None) -> tuple[dict[str, np.ndarray], dict[str, Any]]: + """ + Reset the environment to its initial state. + This method resets the step counter and clears any episodic data. + + Args: + seed: A seed for random number generation to ensure reproducibility. + options: Additional options to influence the reset behavior. + + Returns: + A tuple containing: + - observation (dict): The initial sensor observation. + - info (dict): A dictionary with supplementary information, including the key "is_intervention". + """ + super().reset(seed=seed, options=options) + + self.robot.reset() + + # Capture the initial observation. + observation = self._get_observation() + + # Reset episode tracking variables. + self.current_step = 0 + self.episode_data = None + + return observation, {"is_intervention": False} + + def step(self, action) -> tuple[dict[str, np.ndarray], float, bool, bool, dict[str, Any]]: + """ + Execute a single step within the environment using the specified action. + + The provided action is processed and sent to the robot as joint position commands + that may be either absolute values or deltas based on the environment configuration. + + Args: + action: The commanded joint positions as a numpy array or torch tensor. + + Returns: + A tuple containing: + - observation (dict): The new sensor observation after taking the step. + - reward (float): The step reward (default is 0.0 within this wrapper). + - terminated (bool): True if the episode has reached a terminal state. + - truncated (bool): True if the episode was truncated (e.g., time constraints). + - info (dict): Additional debugging information including intervention status. + """ + self.current_joint_positions = self._get_observation()["agent_pos"] + + action_dict = {"delta_x": action[0], "delta_y": action[1], "delta_z": action[2]} + + # 1.0 action corresponds to no-op action + action_dict["gripper"] = action[3] if self.use_gripper else 1.0 + + self.robot.send_action(action_dict) + + if self.display_cameras: + self.render() + + self.current_step += 1 + + reward = 0.0 + terminated = False + truncated = False + + return ( + self._get_observation(), + reward, + terminated, + truncated, + {"is_intervention": False}, + ) + + def render(self): + """ + Render the current state of the environment by displaying the robot's camera feeds. + """ + import cv2 + + observation = self._get_observation() + image_keys = [key for key in observation if "image" in key] + + for key in image_keys: + cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR)) + cv2.waitKey(1) + + def close(self): + """ + Close the environment and clean up resources by disconnecting the robot. + + If the robot is currently connected, this method properly terminates the connection to ensure that all + associated resources are released. + """ + if self.robot.is_connected: + self.robot.disconnect() + + +class AddJointVelocityToObservation(gym.ObservationWrapper): + """ + Wrapper that adds joint velocity information to the observation. + + This wrapper computes joint velocities by tracking changes in joint positions over time, + and extends the observation space to include these velocities. + """ + + def __init__(self, env, joint_velocity_limits=100.0, fps=30, num_dof=6): + """ + Initialize the joint velocity wrapper. + + Args: + env: The environment to wrap. + joint_velocity_limits: Maximum expected joint velocity for space bounds. + fps: Frames per second used to calculate velocity (position delta / time). + num_dof: Number of degrees of freedom (joints) in the robot. + """ + super().__init__(env) + + # Extend observation space to include joint velocities + old_low = self.observation_space["observation.state"].low + old_high = self.observation_space["observation.state"].high + old_shape = self.observation_space["observation.state"].shape + + self.last_joint_positions = np.zeros(num_dof) + + new_low = np.concatenate([old_low, np.ones(num_dof) * -joint_velocity_limits]) + new_high = np.concatenate([old_high, np.ones(num_dof) * joint_velocity_limits]) + + new_shape = (old_shape[0] + num_dof,) + + self.observation_space["observation.state"] = gym.spaces.Box( + low=new_low, + high=new_high, + shape=new_shape, + dtype=np.float32, + ) + + self.dt = 1.0 / fps + + def observation(self, observation): + """ + Add joint velocity information to the observation. + + Args: + observation: The original observation from the environment. + + Returns: + The modified observation with joint velocities. + """ + joint_velocities = (observation["agent_pos"] - self.last_joint_positions) / self.dt + self.last_joint_positions = observation["agent_pos"] + observation["agent_pos"] = np.concatenate([observation["agent_pos"], joint_velocities], axis=-1) + return observation + + +class AddCurrentToObservation(gym.ObservationWrapper): + """ + Wrapper that adds motor current information to the observation. + + This wrapper extends the observation space to include the current values + from each motor, providing information about the forces being applied. + """ + + def __init__(self, env, max_current=500, num_dof=6): + """ + Initialize the current observation wrapper. + + Args: + env: The environment to wrap. + max_current: Maximum expected current for space bounds. + num_dof: Number of degrees of freedom (joints) in the robot. + """ + super().__init__(env) + + # Extend observation space to include joint velocities + old_low = self.observation_space["observation.state"].low + old_high = self.observation_space["observation.state"].high + old_shape = self.observation_space["observation.state"].shape + + new_low = np.concatenate([old_low, np.zeros(num_dof)]) + new_high = np.concatenate([old_high, np.ones(num_dof) * max_current]) + + new_shape = (old_shape[0] + num_dof,) + + self.observation_space["observation.state"] = gym.spaces.Box( + low=new_low, + high=new_high, + shape=new_shape, + dtype=np.float32, + ) + + def observation(self, observation): + """ + Add current information to the observation. + + Args: + observation: The original observation from the environment. + + Returns: + The modified observation with current values. + """ + present_current_observation = self.unwrapped._get_observation()["agent_pos"] + observation["agent_pos"] = np.concatenate( + [observation["agent_pos"], present_current_observation], axis=-1 + ) + return observation + + +class RewardWrapper(gym.Wrapper): + def __init__(self, env, reward_classifier, device="cuda"): + """ + Wrapper to add reward prediction to the environment using a trained classifier. + + Args: + env: The environment to wrap. + reward_classifier: The reward classifier model. + device: The device to run the model on. + """ + self.env = env + + self.device = device + + self.reward_classifier = torch.compile(reward_classifier) + self.reward_classifier.to(self.device) + + def step(self, action): + """ + Execute a step and compute the reward using the classifier. + + Args: + action: The action to take in the environment. + + Returns: + Tuple of (observation, reward, terminated, truncated, info). + """ + observation, _, terminated, truncated, info = self.env.step(action) + + images = {} + for key in observation: + if "image" in key: + images[key] = observation[key].to(self.device, non_blocking=(self.device == "cuda")) + if images[key].dim() == 3: + images[key] = images[key].unsqueeze(0) + + start_time = time.perf_counter() + with torch.inference_mode(): + success = ( + self.reward_classifier.predict_reward(images, threshold=0.7) + if self.reward_classifier is not None + else 0.0 + ) + info["Reward classifier frequency"] = 1 / (time.perf_counter() - start_time) + + reward = 0.0 + if success == 1.0: + terminated = True + reward = 1.0 + + return observation, reward, terminated, truncated, info + + def reset(self, seed=None, options=None): + """ + Reset the environment. + + Args: + seed: Random seed for reproducibility. + options: Additional reset options. + + Returns: + The initial observation and info from the wrapped environment. + """ + return self.env.reset(seed=seed, options=options) + + +class TimeLimitWrapper(gym.Wrapper): + """ + Wrapper that adds a time limit to episodes and tracks execution time. + + This wrapper terminates episodes after a specified time has elapsed, providing + better control over episode length. + """ + + def __init__(self, env, control_time_s, fps): + """ + Initialize the time limit wrapper. + + Args: + env: The environment to wrap. + control_time_s: Maximum episode duration in seconds. + fps: Frames per second for calculating the maximum number of steps. + """ + self.env = env + self.control_time_s = control_time_s + self.fps = fps + + self.last_timestamp = 0.0 + self.episode_time_in_s = 0.0 + + self.max_episode_steps = int(self.control_time_s * self.fps) + + self.current_step = 0 + + def step(self, action): + """ + Step the environment and track time elapsed. + + Args: + action: The action to take in the environment. + + Returns: + Tuple of (observation, reward, terminated, truncated, info). + """ + obs, reward, terminated, truncated, info = self.env.step(action) + time_since_last_step = time.perf_counter() - self.last_timestamp + self.episode_time_in_s += time_since_last_step + self.last_timestamp = time.perf_counter() + self.current_step += 1 + # check if last timestep took more time than the expected fps + if 1.0 / time_since_last_step < self.fps: + logging.debug(f"Current timestep exceeded expected fps {self.fps}") + + if self.current_step >= self.max_episode_steps: + terminated = True + return obs, reward, terminated, truncated, info + + def reset(self, seed=None, options=None): + """ + Reset the environment and time tracking. + + Args: + seed: Random seed for reproducibility. + options: Additional reset options. + + Returns: + The initial observation and info from the wrapped environment. + """ + self.episode_time_in_s = 0.0 + self.last_timestamp = time.perf_counter() + self.current_step = 0 + return self.env.reset(seed=seed, options=options) + + +class ImageCropResizeWrapper(gym.Wrapper): + """ + Wrapper that crops and resizes image observations. + + This wrapper processes image observations to focus on relevant regions by + cropping and then resizing to a standard size. + """ + + def __init__( + self, + env, + crop_params_dict: dict[str, Annotated[tuple[int], 4]], + resize_size=None, + ): + """ + Initialize the image crop and resize wrapper. + + Args: + env: The environment to wrap. + crop_params_dict: Dictionary mapping image observation keys to crop parameters + (top, left, height, width). + resize_size: Target size for resized images (height, width). Defaults to (128, 128). + """ + super().__init__(env) + self.env = env + self.crop_params_dict = crop_params_dict + print(f"obs_keys , {self.env.observation_space}") + print(f"crop params dict {crop_params_dict.keys()}") + for key_crop in crop_params_dict: + if key_crop not in self.env.observation_space.keys(): # noqa: SIM118 + raise ValueError(f"Key {key_crop} not in observation space") + for key in crop_params_dict: + new_shape = (3, resize_size[0], resize_size[1]) + self.observation_space[key] = gym.spaces.Box(low=0, high=255, shape=new_shape) + + self.resize_size = resize_size + if self.resize_size is None: + self.resize_size = (128, 128) + + def step(self, action): + """ + Step the environment and process image observations. + + Args: + action: The action to take in the environment. + + Returns: + Tuple of (observation, reward, terminated, truncated, info) with processed images. + """ + obs, reward, terminated, truncated, info = self.env.step(action) + for k in self.crop_params_dict: + device = obs[k].device + if obs[k].dim() >= 3: + # Reshape to combine height and width dimensions for easier calculation + batch_size = obs[k].size(0) + channels = obs[k].size(1) + flattened_spatial_dims = obs[k].view(batch_size, channels, -1) + + # Calculate standard deviation across spatial dimensions (H, W) + # If any channel has std=0, all pixels in that channel have the same value + # This is helpful if one camera mistakenly covered or the image is black + std_per_channel = torch.std(flattened_spatial_dims, dim=2) + if (std_per_channel <= 0.02).any(): + logging.warning( + f"Potential hardware issue detected: All pixels have the same value in observation {k}" + ) + + if device == torch.device("mps:0"): + obs[k] = obs[k].cpu() + + obs[k] = F.crop(obs[k], *self.crop_params_dict[k]) + obs[k] = F.resize(obs[k], self.resize_size) + # TODO (michel-aractingi): Bug in resize, it returns values outside [0, 1] + obs[k] = obs[k].clamp(0.0, 1.0) + obs[k] = obs[k].to(device) + + return obs, reward, terminated, truncated, info + + def reset(self, seed=None, options=None): + """ + Reset the environment and process image observations. + + Args: + seed: Random seed for reproducibility. + options: Additional reset options. + + Returns: + Tuple of (observation, info) with processed images. + """ + obs, info = self.env.reset(seed=seed, options=options) + for k in self.crop_params_dict: + device = obs[k].device + if device == torch.device("mps:0"): + obs[k] = obs[k].cpu() + obs[k] = F.crop(obs[k], *self.crop_params_dict[k]) + obs[k] = F.resize(obs[k], self.resize_size) + obs[k] = obs[k].clamp(0.0, 1.0) + obs[k] = obs[k].to(device) + return obs, info + + +class ConvertToLeRobotObservation(gym.ObservationWrapper): + """ + Wrapper that converts standard observations to LeRobot format. + + This wrapper processes observations to match the expected format for LeRobot, + including normalizing image values and moving tensors to the specified device. + """ + + def __init__(self, env, device: str = "cpu"): + """ + Initialize the LeRobot observation converter. + + Args: + env: The environment to wrap. + device: Target device for the observation tensors. + """ + super().__init__(env) + + self.device = torch.device(device) + + def observation(self, observation): + """ + Convert observations to LeRobot format. + + Args: + observation: The original observation from the environment. + + Returns: + The processed observation with normalized images and proper tensor formats. + """ + observation = preprocess_observation(observation) + observation = { + key: observation[key].to(self.device, non_blocking=self.device.type == "cuda") + for key in observation + } + return observation + + +class ResetWrapper(gym.Wrapper): + """ + Wrapper that handles environment reset procedures. + + This wrapper provides additional functionality during environment reset, + including the option to reset to a fixed pose or allow manual reset. + """ + + def __init__( + self, + env: RobotEnv, + reset_pose: np.ndarray | None = None, + reset_time_s: float = 5, + ): + """ + Initialize the reset wrapper. + + Args: + env: The environment to wrap. + reset_pose: Fixed joint positions to reset to. If None, manual reset is used. + reset_time_s: Time in seconds to wait after reset or allowed for manual reset. + """ + super().__init__(env) + self.reset_time_s = reset_time_s + self.reset_pose = reset_pose + self.robot = self.unwrapped.robot + + def reset(self, *, seed=None, options=None): + """ + Reset the environment with either fixed or manual reset procedure. + + If reset_pose is provided, the robot will move to that position. + Otherwise, manual teleoperation control is allowed for reset_time_s seconds. + + Args: + seed: Random seed for reproducibility. + options: Additional reset options. + + Returns: + The initial observation and info from the wrapped environment. + """ + start_time = time.perf_counter() + if self.reset_pose is not None: + log_say("Reset the environment.", play_sounds=True) + reset_follower_position(self.unwrapped.robot, self.reset_pose) + log_say("Reset the environment done.", play_sounds=True) + + if hasattr(self.env, "robot_leader"): + self.env.robot_leader.bus.sync_write("Torque_Enable", 1) + log_say("Reset the leader robot.", play_sounds=True) + reset_follower_position(self.env.robot_leader, self.reset_pose) + log_say("Reset the leader robot done.", play_sounds=True) + else: + log_say( + f"Manually reset the environment for {self.reset_time_s} seconds.", + play_sounds=True, + ) + start_time = time.perf_counter() + while time.perf_counter() - start_time < self.reset_time_s: + action = self.env.robot_leader.get_action() + self.unwrapped.robot.send_action(action) + + log_say("Manual reset of the environment done.", play_sounds=True) + + busy_wait(self.reset_time_s - (time.perf_counter() - start_time)) + + return super().reset(seed=seed, options=options) + + +class BatchCompatibleWrapper(gym.ObservationWrapper): + """ + Wrapper that ensures observations are compatible with batch processing. + + This wrapper adds a batch dimension to observations that don't already have one, + making them compatible with models that expect batched inputs. + """ + + def __init__(self, env): + """ + Initialize the batch compatibility wrapper. + + Args: + env: The environment to wrap. + """ + super().__init__(env) + + def observation(self, observation: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + """ + Add batch dimensions to observations if needed. + + Args: + observation: Dictionary of observation tensors. + + Returns: + Dictionary of observation tensors with batch dimensions. + """ + for key in observation: + if "image" in key and observation[key].dim() == 3: + observation[key] = observation[key].unsqueeze(0) + if "state" in key and observation[key].dim() == 1: + observation[key] = observation[key].unsqueeze(0) + if "velocity" in key and observation[key].dim() == 1: + observation[key] = observation[key].unsqueeze(0) + return observation + + +class GripperPenaltyWrapper(gym.RewardWrapper): + """ + Wrapper that adds penalties for inefficient gripper commands. + + This wrapper modifies rewards to discourage excessive gripper movement + or commands that attempt to move the gripper beyond its physical limits. + """ + + def __init__(self, env, penalty: float = -0.1): + """ + Initialize the gripper penalty wrapper. + + Args: + env: The environment to wrap. + penalty: Negative reward value to apply for inefficient gripper actions. + """ + super().__init__(env) + self.penalty = penalty + self.last_gripper_state = None + + def reward(self, reward, action): + """ + Apply penalties to reward based on gripper actions. + + Args: + reward: The original reward from the environment. + action: The action that was taken. + + Returns: + Modified reward with penalty applied if necessary. + """ + gripper_state_normalized = self.last_gripper_state / self.unwrapped.robot.config.max_gripper_pos + + action_normalized = action - 1.0 # action / MAX_GRIPPER_COMMAND + + gripper_penalty_bool = (gripper_state_normalized < 0.5 and action_normalized > 0.5) or ( + gripper_state_normalized > 0.75 and action_normalized < -0.5 + ) + + return reward + self.penalty * int(gripper_penalty_bool) + + def step(self, action): + """ + Step the environment and apply gripper penalties. + + Args: + action: The action to take in the environment. + + Returns: + Tuple of (observation, reward, terminated, truncated, info) with penalty applied. + """ + self.last_gripper_state = self.unwrapped.robot.bus.sync_read("Present_Position")["gripper"] + + gripper_action = action[-1] + obs, reward, terminated, truncated, info = self.env.step(action) + gripper_penalty = self.reward(reward, gripper_action) + + info["discrete_penalty"] = gripper_penalty + + return obs, reward, terminated, truncated, info + + def reset(self, **kwargs): + """ + Reset the environment and penalty tracking. + + Args: + **kwargs: Keyword arguments passed to the wrapped environment's reset. + + Returns: + The initial observation and info with gripper penalty initialized. + """ + self.last_gripper_state = None + obs, info = super().reset(**kwargs) + info["gripper_penalty"] = 0.0 + return obs, info + + +class GripperActionWrapper(gym.ActionWrapper): + """ + Wrapper that processes gripper control commands. + + This wrapper quantizes and processes gripper commands, adding a sleep time between + consecutive gripper actions to prevent rapid toggling. + """ + + def __init__(self, env, quantization_threshold: float = 0.2, gripper_sleep: float = 0.0): + """ + Initialize the gripper action wrapper. + + Args: + env: The environment to wrap. + quantization_threshold: Threshold below which gripper commands are quantized to zero. + gripper_sleep: Minimum time in seconds between consecutive gripper commands. + """ + super().__init__(env) + self.quantization_threshold = quantization_threshold + self.gripper_sleep = gripper_sleep + self.last_gripper_action_time = 0.0 + self.last_gripper_action = None + + def action(self, action): + """ + Process gripper commands in the action. + + Args: + action: The original action from the agent. + + Returns: + Modified action with processed gripper command. + """ + if self.gripper_sleep > 0.0: + if ( + self.last_gripper_action is not None + and time.perf_counter() - self.last_gripper_action_time < self.gripper_sleep + ): + action[-1] = self.last_gripper_action + else: + self.last_gripper_action_time = time.perf_counter() + self.last_gripper_action = action[-1] + + gripper_command = action[-1] + # Gripper actions are between 0, 2 + # we want to quantize them to -1, 0 or 1 + gripper_command = gripper_command - 1.0 + + if self.quantization_threshold is not None: + # Quantize gripper command to -1, 0 or 1 + gripper_command = ( + np.sign(gripper_command) if abs(gripper_command) > self.quantization_threshold else 0.0 + ) + gripper_command = gripper_command * self.unwrapped.robot.config.max_gripper_pos + + gripper_state = self.unwrapped.robot.bus.sync_read("Present_Position")["gripper"] + + gripper_action_value = np.clip( + gripper_state + gripper_command, 0, self.unwrapped.robot.config.max_gripper_pos + ) + action[-1] = gripper_action_value.item() + return action + + def reset(self, **kwargs): + """ + Reset the gripper action tracking. + + Args: + **kwargs: Keyword arguments passed to the wrapped environment's reset. + + Returns: + The initial observation and info. + """ + obs, info = super().reset(**kwargs) + self.last_gripper_action_time = 0.0 + self.last_gripper_action = None + return obs, info + + +class EEObservationWrapper(gym.ObservationWrapper): + """ + Wrapper that adds end-effector pose information to observations. + + This wrapper computes the end-effector pose using forward kinematics + and adds it to the observation space. + """ + + def __init__(self, env, ee_pose_limits): + """ + Initialize the end-effector observation wrapper. + + Args: + env: The environment to wrap. + ee_pose_limits: Dictionary with 'min' and 'max' keys containing limits for EE pose. + """ + super().__init__(env) + + # Extend observation space to include end effector pose + prev_space = self.observation_space["observation.state"] + + self.observation_space["observation.state"] = gym.spaces.Box( + low=np.concatenate([prev_space.low, ee_pose_limits["min"]]), + high=np.concatenate([prev_space.high, ee_pose_limits["max"]]), + shape=(prev_space.shape[0] + 3,), + dtype=np.float32, + ) + + # Initialize kinematics instance for the appropriate robot type + robot_type = getattr(env.unwrapped.robot.config, "robot_type", "so101") + if "so100" in robot_type or "so101" in robot_type: + # Note to be compatible with the rest of the codebase, + # we are using the new calibration method for so101 and so100 + robot_type = "so_new_calibration" + self.kinematics = RobotKinematics(robot_type) + + def observation(self, observation): + """ + Add end-effector pose to the observation. + + Args: + observation: Original observation from the environment. + + Returns: + Enhanced observation with end-effector pose information. + """ + current_joint_pos = self.unwrapped._get_observation()["agent_pos"] + + current_ee_pos = self.kinematics.forward_kinematics(current_joint_pos, frame="gripper_tip")[:3, 3] + observation["agent_pos"] = np.concatenate([observation["agent_pos"], current_ee_pos], -1) + return observation + + +########################################################### +# Wrappers related to human intervention and input devices +########################################################### + + +class BaseLeaderControlWrapper(gym.Wrapper): + """ + Base class for leader-follower robot control wrappers. + + This wrapper enables human intervention through a leader-follower robot setup, + where the human can control a leader robot to guide the follower robot's movements. + """ + + def __init__( + self, + env, + teleop_device, + end_effector_step_sizes, + use_geared_leader_arm: bool = False, + use_gripper=False, + ): + """ + Initialize the base leader control wrapper. + + Args: + env: The environment to wrap. + teleop_device: The teleoperation device. + use_geared_leader_arm: Whether to use a geared leader arm setup. + use_gripper: Whether to include gripper control. + """ + super().__init__(env) + self.robot_leader = teleop_device + self.robot_follower = env.unwrapped.robot + self.use_geared_leader_arm = use_geared_leader_arm + self.use_gripper: bool = use_gripper + self.end_effector_step_sizes = np.array(list(end_effector_step_sizes.values())) + + # Set up keyboard event tracking + self._init_keyboard_events() + self.event_lock = Lock() # Thread-safe access to events + + # Initialize robot control + robot_type = getattr(env.unwrapped.robot.config, "robot_type", "so101") + if "so100" in robot_type or "so101" in robot_type: + # Note to be compatible with the rest of the codebase, + # we are using the new calibration method for so101 and so100 + robot_type = "so_new_calibration" + self.kinematics = RobotKinematics(robot_type) + self.leader_torque_enabled = True + self.prev_leader_gripper = None + + # Configure leader arm + # NOTE: Lower the gains of leader arm for automatic take-over + # With lower gains we can manually move the leader arm without risk of injury to ourselves or the robot + # With higher gains, it would be dangerous and difficult to modify the leader's pose while torque is enabled + # Default value for P_coeff is 32 + self.robot_leader.bus.sync_write("Torque_Enable", 1) + for motor in self.robot_leader.bus.motors: + self.robot_leader.bus.write("P_Coefficient", motor, 16) + self.robot_leader.bus.write("I_Coefficient", motor, 0) + self.robot_leader.bus.write("D_Coefficient", motor, 16) + + self.leader_tracking_error_queue = deque(maxlen=4) + self._init_keyboard_listener() + + def _init_keyboard_events(self): + """ + Initialize the keyboard events dictionary. + + This method sets up tracking for keyboard events used for intervention control. + It should be overridden in subclasses to add additional events. + """ + self.keyboard_events = { + "episode_success": False, + "episode_end": False, + "rerecord_episode": False, + } + + def _handle_key_press(self, key, keyboard): + """ + Handle key press events. + + Args: + key: The key that was pressed. + keyboard: The keyboard module with key definitions. + + This method should be overridden in subclasses for additional key handling. + """ + try: + if key == keyboard.Key.esc: + self.keyboard_events["episode_end"] = True + return + if key == keyboard.Key.left: + self.keyboard_events["rerecord_episode"] = True + return + if hasattr(key, "char") and key.char == "s": + logging.info("Key 's' pressed. Episode success triggered.") + self.keyboard_events["episode_success"] = True + return + except Exception as e: + logging.error(f"Error handling key press: {e}") + + def _init_keyboard_listener(self): + """ + Initialize the keyboard listener for intervention control. + + This method sets up keyboard event handling if not in headless mode. + """ + from pynput import keyboard + + def on_press(key): + with self.event_lock: + self._handle_key_press(key, keyboard) + + self.listener = keyboard.Listener(on_press=on_press) + self.listener.start() + + def _check_intervention(self): + """ + Check if human intervention is needed. + + Returns: + Boolean indicating whether intervention is needed. + + This method should be overridden in subclasses with specific intervention logic. + """ + return False + + def _handle_intervention(self, action): + """ + Process actions during intervention mode. + + Args: + action: The original action from the agent. + + Returns: + Tuple of (modified_action, intervention_action). + """ + if self.leader_torque_enabled: + self.robot_leader.bus.sync_write("Torque_Enable", 0) + self.leader_torque_enabled = False + + leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position") + follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position") + + leader_pos = np.array([leader_pos_dict[name] for name in leader_pos_dict], dtype=np.float32) + follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict], dtype=np.float32) + + self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - leader_pos[:-1])) + + # [:3, 3] Last column of the transformation matrix corresponds to the xyz translation + leader_ee = self.kinematics.forward_kinematics(leader_pos, frame="gripper_tip")[:3, 3] + follower_ee = self.kinematics.forward_kinematics(follower_pos, frame="gripper_tip")[:3, 3] + + action = np.clip(leader_ee - follower_ee, -self.end_effector_step_sizes, self.end_effector_step_sizes) + # Normalize the action to the range [-1, 1] + action = action / self.end_effector_step_sizes + + if self.use_gripper: + if self.prev_leader_gripper is None: + self.prev_leader_gripper = np.clip( + leader_pos[-1], 0, self.robot_follower.config.max_gripper_pos + ) + + # Get gripper action delta based on leader pose + leader_gripper = leader_pos[-1] + gripper_delta = leader_gripper - self.prev_leader_gripper + + # Normalize by max angle and quantize to {0,1,2} + normalized_delta = gripper_delta / self.robot_follower.config.max_gripper_pos + if normalized_delta >= 0.3: + gripper_action = 2 + elif normalized_delta <= 0.1: + gripper_action = 0 + else: + gripper_action = 1 + + action = np.append(action, gripper_action) + + return action + + def _handle_leader_teleoperation(self): + """ + Handle leader teleoperation in non-intervention mode. + + This method synchronizes the leader robot position with the follower. + """ + + prev_leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position") + prev_leader_pos = np.array( + [prev_leader_pos_dict[name] for name in prev_leader_pos_dict], dtype=np.float32 + ) + + if not self.leader_torque_enabled: + self.robot_leader.bus.sync_write("Torque_Enable", 1) + self.leader_torque_enabled = True + + follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position") + follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict], dtype=np.float32) + + goal_pos = {f"{motor}": follower_pos[i] for i, motor in enumerate(self.robot_leader.bus.motors)} + self.robot_leader.bus.sync_write("Goal_Position", goal_pos) + + self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - prev_leader_pos[:-1])) + + def step(self, action): + """ + Execute a step with possible human intervention. + + Args: + action: The action to take in the environment. + + Returns: + Tuple of (observation, reward, terminated, truncated, info). + """ + is_intervention = self._check_intervention() + + # NOTE: + if is_intervention: + action = self._handle_intervention(action) + else: + self._handle_leader_teleoperation() + + # NOTE: + obs, reward, terminated, truncated, info = self.env.step(action) + + # Add intervention info + info["is_intervention"] = is_intervention + info["action_intervention"] = action if is_intervention else None + + self.prev_leader_gripper = np.clip( + self.robot_leader.bus.sync_read("Present_Position")["gripper"], + 0, + self.robot_follower.config.max_gripper_pos, + ) + + # Check for success or manual termination + success = self.keyboard_events["episode_success"] + terminated = terminated or self.keyboard_events["episode_end"] or success + + if success: + reward = 1.0 + logging.info("Episode ended successfully with reward 1.0") + + return obs, reward, terminated, truncated, info + + def reset(self, **kwargs): + """ + Reset the environment and intervention state. + + Args: + **kwargs: Keyword arguments passed to the wrapped environment's reset. + + Returns: + The initial observation and info. + """ + self.keyboard_events = dict.fromkeys(self.keyboard_events, False) + self.leader_tracking_error_queue.clear() + return super().reset(**kwargs) + + def close(self): + """ + Clean up resources, including stopping keyboard listener. + + Returns: + Result of closing the wrapped environment. + """ + if hasattr(self, "listener") and self.listener is not None: + self.listener.stop() + return self.env.close() + + +class GearedLeaderControlWrapper(BaseLeaderControlWrapper): + """ + Wrapper that enables manual intervention via keyboard. + + This wrapper extends the BaseLeaderControlWrapper to allow explicit toggling + of human intervention mode with keyboard controls. + """ + + def _init_keyboard_events(self): + """ + Initialize keyboard events including human intervention flag. + + Extends the base class dictionary with an additional flag for tracking + intervention state toggled by keyboard. + """ + super()._init_keyboard_events() + self.keyboard_events["human_intervention_step"] = False + + def _handle_key_press(self, key, keyboard): + """ + Handle key presses including space for intervention toggle. + + Args: + key: The key that was pressed. + keyboard: The keyboard module with key definitions. + + Extends the base handler to respond to space key for toggling intervention. + """ + super()._handle_key_press(key, keyboard) + if key == keyboard.Key.space: + if not self.keyboard_events["human_intervention_step"]: + logging.info( + "Space key pressed. Human intervention required.\n" + "Place the leader in similar pose to the follower and press space again." + ) + self.keyboard_events["human_intervention_step"] = True + log_say("Human intervention step.", play_sounds=True) + else: + self.keyboard_events["human_intervention_step"] = False + logging.info("Space key pressed for a second time.\nContinuing with policy actions.") + log_say("Continuing with policy actions.", play_sounds=True) + + def _check_intervention(self): + """ + Check if human intervention is active based on keyboard toggle. + + Returns: + Boolean indicating whether intervention mode is active. + """ + return self.keyboard_events["human_intervention_step"] + + +class GearedLeaderAutomaticControlWrapper(BaseLeaderControlWrapper): + """ + Wrapper with automatic intervention based on error thresholds. + + This wrapper monitors the error between leader and follower positions + and automatically triggers intervention when error exceeds thresholds. + """ + + def __init__( + self, + env, + teleop_device, + end_effector_step_sizes, + use_gripper=False, + intervention_threshold=10.0, + release_threshold=1e-2, + ): + """ + Initialize the automatic intervention wrapper. + + Args: + env: The environment to wrap. + teleop_device: The teleoperation device. + use_gripper: Whether to include gripper control. + intervention_threshold: Error threshold to trigger intervention. + release_threshold: Error threshold to release intervention. + queue_size: Number of error measurements to track for smoothing. + """ + super().__init__(env, teleop_device, end_effector_step_sizes, use_gripper=use_gripper) + + # Error tracking parameters + self.intervention_threshold = intervention_threshold # Threshold to trigger intervention + self.release_threshold = release_threshold # Threshold to release intervention + self.is_intervention_active = False + self.start_time = time.perf_counter() + + def _check_intervention(self): + """ + Determine if intervention should occur based on the rate of change of leader-follower error in end_effector space. + + This method monitors the rate of change of leader-follower error in end_effector space + and automatically triggers intervention when the rate of change exceeds + the intervention threshold, releasing when it falls below the release threshold. + + Returns: + Boolean indicating whether intervention should be active. + """ + + # Condition for starting the intervention + # If the error in teleoperation is too high, that means the a user has grasped the leader robot and he wants to take over + if ( + not self.is_intervention_active + and len(self.leader_tracking_error_queue) == self.leader_tracking_error_queue.maxlen + and np.var(list(self.leader_tracking_error_queue)[-2:]) > self.intervention_threshold + ): + self.is_intervention_active = True + self.leader_tracking_error_queue.clear() + log_say("Intervention started", play_sounds=True) + return True + + # Track the error over time in leader_tracking_error_queue + # If the variance of the tracking error is too low, that means the user has let go of the leader robot and the intervention is over + if ( + self.is_intervention_active + and len(self.leader_tracking_error_queue) == self.leader_tracking_error_queue.maxlen + and np.var(self.leader_tracking_error_queue) < self.release_threshold + ): + self.is_intervention_active = False + self.leader_tracking_error_queue.clear() + log_say("Intervention ended", play_sounds=True) + return False + + # If not change has happened that merits a change in the intervention state, return the current state + return self.is_intervention_active + + def reset(self, **kwargs): + """ + Reset error tracking on environment reset. + + Args: + **kwargs: Keyword arguments passed to the wrapped environment's reset. + + Returns: + The initial observation and info. + """ + self.is_intervention_active = False + return super().reset(**kwargs) + + +class GamepadControlWrapper(gym.Wrapper): + """ + Wrapper that allows controlling a gym environment with a gamepad. + + This wrapper intercepts the step method and allows human input via gamepad + to override the agent's actions when desired. + """ + + def __init__( + self, + env, + teleop_device, # Accepts an instantiated teleoperator + use_gripper=False, # This should align with teleop_device's config + auto_reset=False, + ): + """ + Initialize the gamepad controller wrapper. + + Args: + env: The environment to wrap. + teleop_device: The instantiated teleoperation device (e.g., GamepadTeleop). + use_gripper: Whether to include gripper control (should match teleop_device.config.use_gripper). + auto_reset: Whether to auto reset the environment when episode ends. + """ + super().__init__(env) + + self.teleop_device = teleop_device + # Ensure the teleop_device is connected if it has a connect method + if hasattr(self.teleop_device, "connect") and not self.teleop_device.is_connected: + self.teleop_device.connect() + + # self.controller attribute is removed + + self.auto_reset = auto_reset + # use_gripper from args should ideally match teleop_device.config.use_gripper + # For now, we use the one passed, but it can lead to inconsistency if not set correctly from config + self.use_gripper = use_gripper + + logging.info("Gamepad control wrapper initialized with provided teleop_device.") + print( + "Gamepad controls (managed by the provided teleop_device - specific button mappings might vary):" + ) + print(" Left analog stick: Move in X-Y plane") + print(" Right analog stick: Move in Z axis (up/down)") + print(" X/Square button: End episode (FAILURE)") + print(" Y/Triangle button: End episode (SUCCESS)") + print(" B/Circle button: Exit program") + + def get_gamepad_action( + self, + ) -> tuple[bool, np.ndarray, bool, bool, bool]: + """ + Get the current action from the gamepad if any input is active. + + Returns: + Tuple containing: + - is_active: Whether gamepad input is active (from teleop_device.gamepad.should_intervene()) + - action: The action derived from gamepad input (from teleop_device.get_action()) + - terminate_episode: Whether episode termination was requested + - success: Whether episode success was signaled + - rerecord_episode: Whether episode rerecording was requested + """ + if not hasattr(self.teleop_device, "gamepad") or self.teleop_device.gamepad is None: + raise AttributeError( + "teleop_device does not have a 'gamepad' attribute or it is None. Expected for GamepadControlWrapper." + ) + + # Get status flags from the underlying gamepad controller within the teleop_device + self.teleop_device.gamepad.update() # Ensure gamepad state is fresh + intervention_is_active = self.teleop_device.gamepad.should_intervene() + episode_end_status = self.teleop_device.gamepad.get_episode_end_status() + + terminate_episode = episode_end_status is not None + success = episode_end_status == "success" + rerecord_episode = episode_end_status == "rerecord_episode" + + # Get the action dictionary from the teleop_device + action_dict = self.teleop_device.get_action() + + # Convert action_dict to numpy array based on expected structure + # Order: delta_x, delta_y, delta_z, gripper (if use_gripper) + action_list = [action_dict["delta_x"], action_dict["delta_y"], action_dict["delta_z"]] + if self.use_gripper: + # GamepadTeleop returns gripper action as 0 (close), 1 (stay), 2 (open) + # This needs to be consistent with what EEActionWrapper expects if it's used downstream + # EEActionWrapper for gripper typically expects 0.0 (closed) to 2.0 (open) + # For now, we pass the direct value from GamepadTeleop, ensure downstream compatibility. + gripper_val = action_dict.get("gripper", 1.0) # Default to 1.0 (stay) if not present + action_list.append(float(gripper_val)) + + gamepad_action_np = np.array(action_list, dtype=np.float32) + + return ( + intervention_is_active, + gamepad_action_np, + terminate_episode, + success, + rerecord_episode, + ) + + def step(self, action): + """ + Step the environment, using gamepad input to override actions when active. + + Args: + action: Original action from agent. + + Returns: + Tuple of (observation, reward, terminated, truncated, info). + """ + # Get gamepad state and action + ( + is_intervention, + gamepad_action, + terminate_episode, + success, + rerecord_episode, + ) = self.get_gamepad_action() + + # Update episode ending state if requested + if terminate_episode: + logging.info(f"Episode manually ended: {'SUCCESS' if success else 'FAILURE'}") + + # Only override the action if gamepad is active + action = gamepad_action if is_intervention else action + + # Step the environment + obs, reward, terminated, truncated, info = self.env.step(action) + + # Add episode ending if requested via gamepad + terminated = terminated or truncated or terminate_episode + + if success: + reward = 1.0 + logging.info("Episode ended successfully with reward 1.0") + + if isinstance(action, np.ndarray): + action = torch.from_numpy(action) + + info["is_intervention"] = is_intervention + # The original `BaseLeaderControlWrapper` puts `action_intervention` in info. + # For Gamepad, if intervention, `gamepad_action` is the intervention. + # If not intervention, policy's action is `action`. + # For consistency, let's store the *human's* action if intervention occurred. + info["action_intervention"] = action + + info["rerecord_episode"] = rerecord_episode + + # If episode ended, reset the state + if terminated or truncated: + # Add success/failure information to info dict + info["next.success"] = success + + # Auto reset if configured + if self.auto_reset: + obs, reset_info = self.reset() + info.update(reset_info) + + return obs, reward, terminated, truncated, info + + def close(self): + """ + Clean up resources when environment closes. + + Returns: + Result of closing the wrapped environment. + """ + if hasattr(self.teleop_device, "disconnect"): + self.teleop_device.disconnect() + + # Call the parent close method + return self.env.close() + + +class GymHilDeviceWrapper(gym.Wrapper): + def __init__(self, env, device="cpu"): + super().__init__(env) + self.device = device + + def step(self, action): + obs, reward, terminated, truncated, info = self.env.step(action) + for k in obs: + obs[k] = obs[k].to(self.device) + if "action_intervention" in info: + # NOTE: This is a hack to ensure the action intervention is a float32 tensor and supported on MPS device + info["action_intervention"] = info["action_intervention"].astype(np.float32) + info["action_intervention"] = torch.from_numpy(info["action_intervention"]).to(self.device) + return obs, reward, terminated, truncated, info + + def reset(self, *, seed: int | None = None, options: dict[str, Any] | None = None): + obs, info = self.env.reset(seed=seed, options=options) + for k in obs: + obs[k] = obs[k].to(self.device) + if "action_intervention" in info: + # NOTE: This is a hack to ensure the action intervention is a float32 tensor and supported on MPS device + info["action_intervention"] = info["action_intervention"].astype(np.float32) + info["action_intervention"] = torch.from_numpy(info["action_intervention"]).to(self.device) + return obs, info + + +class GymHilObservationProcessorWrapper(gym.ObservationWrapper): + def __init__(self, env: gym.Env): + super().__init__(env) + prev_space = self.observation_space + new_space = {} + + for key in prev_space: + if "pixels" in key: + for k in prev_space["pixels"]: + new_space[f"observation.images.{k}"] = gym.spaces.Box( + 0.0, 255.0, shape=(3, 128, 128), dtype=np.uint8 + ) + + if key == "agent_pos": + new_space["observation.state"] = prev_space["agent_pos"] + + self.observation_space = gym.spaces.Dict(new_space) + + def observation(self, observation: dict[str, Any]) -> dict[str, Any]: + return preprocess_observation(observation) + + +########################################################### +# Factory functions +########################################################### + + +def make_robot_env(cfg: EnvConfig) -> gym.Env: + """ + Factory function to create a robot environment. + + This function builds a robot environment with all necessary wrappers + based on the provided configuration. + + Args: + cfg: Configuration object containing environment parameters. + + Returns: + A gym environment with all necessary wrappers applied. + """ + if cfg.type == "hil": + import gym_hil # noqa: F401 + + # TODO (azouitine) + env = gym.make( + f"gym_hil/{cfg.task}", + image_obs=True, + render_mode="human", + use_gripper=cfg.wrapper.use_gripper, + gripper_penalty=cfg.wrapper.gripper_penalty, + ) + env = GymHilObservationProcessorWrapper(env=env) + env = GymHilDeviceWrapper(env=env, device=cfg.device) + env = BatchCompatibleWrapper(env=env) + env = TorchActionWrapper(env=env, device=cfg.device) + return env + + if not hasattr(cfg, "robot") or not hasattr(cfg, "teleop"): + raise ValueError( + "Configuration for 'gym_manipulator' must be HILSerlRobotEnvConfig with robot and teleop." + ) + + if cfg.robot is None: + raise ValueError("RobotConfig (cfg.robot) must be provided for gym_manipulator environment.") + robot = make_robot_from_config(cfg.robot) + + teleop_device = make_teleoperator_from_config(cfg.teleop) + teleop_device.connect() + + # Create base environment + env = RobotEnv( + robot=robot, + use_gripper=cfg.wrapper.use_gripper, + display_cameras=cfg.wrapper.display_cameras if cfg.wrapper else False, + ) + + # Add observation and image processing + if cfg.wrapper: + if cfg.wrapper.add_joint_velocity_to_observation: + env = AddJointVelocityToObservation(env=env, fps=cfg.fps) + if cfg.wrapper.add_current_to_observation: + env = AddCurrentToObservation(env=env) + if cfg.wrapper.add_ee_pose_to_observation: + env = EEObservationWrapper(env=env, ee_pose_limits=robot.end_effector_bounds) + + env = ConvertToLeRobotObservation(env=env, device=cfg.device) + + if cfg.wrapper and cfg.wrapper.crop_params_dict is not None: + env = ImageCropResizeWrapper( + env=env, + crop_params_dict=cfg.wrapper.crop_params_dict, + resize_size=cfg.wrapper.resize_size, + ) + + # Add reward computation and control wrappers + reward_classifier = init_reward_classifier(cfg) + if reward_classifier is not None: + env = RewardWrapper(env=env, reward_classifier=reward_classifier, device=cfg.device) + + env = TimeLimitWrapper(env=env, control_time_s=cfg.wrapper.control_time_s, fps=cfg.fps) + if cfg.wrapper.use_gripper and cfg.wrapper.gripper_penalty is not None: + env = GripperPenaltyWrapper( + env=env, + penalty=cfg.wrapper.gripper_penalty, + ) + + # Control mode specific wrappers + control_mode = cfg.wrapper.control_mode + if control_mode == "gamepad": + assert isinstance(teleop_device, GamepadTeleop), ( + "teleop_device must be an instance of GamepadTeleop for gamepad control mode" + ) + env = GamepadControlWrapper( + env=env, + teleop_device=teleop_device, + use_gripper=cfg.wrapper.use_gripper, + ) + elif control_mode == "leader": + env = GearedLeaderControlWrapper( + env=env, + teleop_device=teleop_device, + end_effector_step_sizes=cfg.robot.end_effector_step_sizes, + use_gripper=cfg.wrapper.use_gripper, + ) + elif control_mode == "leader_automatic": + env = GearedLeaderAutomaticControlWrapper( + env=env, + teleop_device=teleop_device, + end_effector_step_sizes=cfg.robot.end_effector_step_sizes, + use_gripper=cfg.wrapper.use_gripper, + ) + else: + raise ValueError(f"Invalid control mode: {control_mode}") + + env = ResetWrapper( + env=env, + reset_pose=cfg.wrapper.fixed_reset_joint_positions, + reset_time_s=cfg.wrapper.reset_time_s, + ) + + env = BatchCompatibleWrapper(env=env) + env = TorchActionWrapper(env=env, device=cfg.device) + + return env + + +def init_reward_classifier(cfg): + """ + Load a reward classifier policy from a pretrained path if configured. + + Args: + cfg: The environment configuration containing classifier paths. + + Returns: + The loaded classifier model or None if not configured. + """ + if cfg.reward_classifier_pretrained_path is None: + return None + + from lerobot.common.policies.sac.reward_model.modeling_classifier import Classifier + + # Get device from config or default to CUDA + device = getattr(cfg, "device", "cpu") + + # Load the classifier directly using from_pretrained + classifier = Classifier.from_pretrained( + pretrained_name_or_path=cfg.reward_classifier_pretrained_path, + ) + + # Ensure model is on the correct device + classifier.to(device) + classifier.eval() # Set to evaluation mode + + return classifier + + +########################################################### +# Record and replay functions +########################################################### + + +def record_dataset(env, policy, cfg): + """ + Record a dataset of robot interactions using either a policy or teleop. + + This function runs episodes in the environment and records the observations, + actions, and results for dataset creation. + + Args: + env: The environment to record from. + policy: Optional policy to generate actions (if None, uses teleop). + cfg: Configuration object containing recording parameters like: + - repo_id: Repository ID for dataset storage + - dataset_root: Local root directory for dataset + - num_episodes: Number of episodes to record + - fps: Frames per second for recording + - push_to_hub: Whether to push dataset to Hugging Face Hub + - task: Name/description of the task being recorded + - number_of_steps_after_success: Number of additional steps to continue recording after + a success (reward=1) is detected. This helps collect + more positive examples for reward classifier training. + """ + from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + + # Setup initial action (zero action if using teleop) + action = env.action_space.sample() * 0.0 + + action_names = ["delta_x_ee", "delta_y_ee", "delta_z_ee"] + if cfg.wrapper.use_gripper: + action_names.append("gripper_delta") + + # Configure dataset features based on environment spaces + features = { + "observation.state": { + "dtype": "float32", + "shape": env.observation_space["observation.state"].shape, + "names": None, + }, + "action": { + "dtype": "float32", + "shape": (len(action_names),), + "names": action_names, + }, + "next.reward": {"dtype": "float32", "shape": (1,), "names": None}, + "next.done": {"dtype": "bool", "shape": (1,), "names": None}, + "complementary_info.discrete_penalty": { + "dtype": "float32", + "shape": (1,), + "names": ["discrete_penalty"], + }, + } + + # Add image features + for key in env.observation_space: + if "image" in key: + features[key] = { + "dtype": "video", + "shape": env.observation_space[key].shape, + "names": ["channels", "height", "width"], + } + + # Create dataset + dataset = LeRobotDataset.create( + cfg.repo_id, + cfg.fps, + root=cfg.dataset_root, + use_videos=True, + image_writer_threads=4, + image_writer_processes=0, + features=features, + ) + + # Record episodes + episode_index = 0 + recorded_action = None + while episode_index < cfg.num_episodes: + obs, _ = env.reset() + start_episode_t = time.perf_counter() + log_say(f"Recording episode {episode_index}", play_sounds=True) + + # Track success state collection + success_detected = False + success_steps_collected = 0 + + # Run episode steps + while time.perf_counter() - start_episode_t < cfg.wrapper.control_time_s: + start_loop_t = time.perf_counter() + + # Get action from policy if available + if cfg.pretrained_policy_name_or_path is not None: + action = policy.select_action(obs) + + # Step environment + obs, reward, terminated, truncated, info = env.step(action) + + # Check if episode needs to be rerecorded + if info.get("rerecord_episode", False): + break + + # For teleop, get action from intervention + recorded_action = { + "action": info["action_intervention"].cpu().squeeze(0).float() if policy is None else action + } + + # Process observation for dataset + obs_processed = {k: v.cpu().squeeze(0).float() for k, v in obs.items()} + + # Check if we've just detected success + if reward == 1.0 and not success_detected: + success_detected = True + logging.info("Success detected! Collecting additional success states.") + + # Add frame to dataset - continue marking as success even during extra collection steps + frame = {**obs_processed, **recorded_action} + + # If we're in the success collection phase, keep marking rewards as 1.0 + if success_detected: + frame["next.reward"] = np.array([1.0], dtype=np.float32) + else: + frame["next.reward"] = np.array([reward], dtype=np.float32) + + # Only mark as done if we're truly done (reached end or collected enough success states) + really_done = terminated or truncated + if success_detected: + success_steps_collected += 1 + really_done = success_steps_collected >= cfg.number_of_steps_after_success + + frame["next.done"] = np.array([really_done], dtype=bool) + frame["complementary_info.discrete_penalty"] = torch.tensor( + [info.get("discrete_penalty", 0.0)], dtype=torch.float32 + ) + dataset.add_frame(frame, task=cfg.task) + + # Maintain consistent timing + if cfg.fps: + dt_s = time.perf_counter() - start_loop_t + busy_wait(1 / cfg.fps - dt_s) + + # Check if we should end the episode + if (terminated or truncated) and not success_detected: + # Regular termination without success + break + elif success_detected and success_steps_collected >= cfg.number_of_steps_after_success: + # We've collected enough success states + logging.info(f"Collected {success_steps_collected} additional success states") + break + + # Handle episode recording + if info.get("rerecord_episode", False): + dataset.clear_episode_buffer() + logging.info(f"Re-recording episode {episode_index}") + continue + + dataset.save_episode() + episode_index += 1 + + # Finalize dataset + # dataset.consolidate(run_compute_stats=True) + if cfg.push_to_hub: + dataset.push_to_hub() + + +def replay_episode(env, cfg): + """ + Replay a recorded episode in the environment. + + This function loads actions from a previously recorded episode + and executes them in the environment. + + Args: + env: The environment to replay in. + cfg: Configuration object containing replay parameters: + - repo_id: Repository ID for dataset + - dataset_root: Local root directory for dataset + - episode: Episode ID to replay + """ + from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + + dataset = LeRobotDataset(cfg.repo_id, root=cfg.dataset_root, episodes=[cfg.episode]) + env.reset() + + actions = dataset.hf_dataset.select_columns("action") + + for idx in range(dataset.num_frames): + start_episode_t = time.perf_counter() + + action = actions[idx]["action"] + env.step(action) + + dt_s = time.perf_counter() - start_episode_t + busy_wait(1 / 10 - dt_s) + + +@parser.wrap() +def main(cfg: EnvConfig): + """Main entry point for the robot environment script. + + This function runs the robot environment in one of several modes + based on the provided configuration. + + Args: + cfg: Configuration object defining the run parameters, + including mode (record, replay, random) and other settings. + """ + env = make_robot_env(cfg) + + if cfg.mode == "record": + policy = None + if cfg.pretrained_policy_name_or_path is not None: + from lerobot.common.policies.sac.modeling_sac import SACPolicy + + policy = SACPolicy.from_pretrained(cfg.pretrained_policy_name_or_path) + policy.to(cfg.device) + policy.eval() + + record_dataset( + env, + policy=policy, + cfg=cfg, + ) + exit() + + if cfg.mode == "replay": + replay_episode( + env, + cfg=cfg, + ) + exit() + + env.reset() + + # Initialize the smoothed action as a random sample. + smoothed_action = env.action_space.sample() * 0.0 + + # Smoothing coefficient (alpha) defines how much of the new random sample to mix in. + # A value close to 0 makes the trajectory very smooth (slow to change), while a value close to 1 is less smooth. + alpha = 1.0 + + num_episode = 0 + successes = [] + while num_episode < 10: + start_loop_s = time.perf_counter() + # Sample a new random action from the robot's action space. + new_random_action = env.action_space.sample() + # Update the smoothed action using an exponential moving average. + smoothed_action = alpha * new_random_action + (1 - alpha) * smoothed_action + + # Execute the step: wrap the NumPy action in a torch tensor. + obs, reward, terminated, truncated, info = env.step(smoothed_action) + if terminated or truncated: + successes.append(reward) + env.reset() + num_episode += 1 + + dt_s = time.perf_counter() - start_loop_s + busy_wait(1 / cfg.fps - dt_s) + + logging.info(f"Success after 20 steps {successes}") + logging.info(f"success rate {sum(successes) / len(successes)}") + + +if __name__ == "__main__": + main() diff --git a/lerobot/scripts/rl/learner.py b/lerobot/scripts/rl/learner.py new file mode 100644 index 0000000000000000000000000000000000000000..d120033c0f41ec327d5c432fb025ee2193b56ea7 --- /dev/null +++ b/lerobot/scripts/rl/learner.py @@ -0,0 +1,1206 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Learner server runner for distributed HILSerl robot policy training. + +This script implements the learner component of the distributed HILSerl architecture. +It initializes the policy network, maintains replay buffers, and updates +the policy based on transitions received from the actor server. + +Examples of usage: + +- Start a learner server for training: +```bash +python lerobot/scripts/rl/learner.py --config_path lerobot/configs/train_config_hilserl_so100.json +``` + +**NOTE**: Start the learner server before launching the actor server. The learner opens a gRPC server +to communicate with actors. + +**NOTE**: Training progress can be monitored through Weights & Biases if wandb.enable is set to true +in your configuration. + +**WORKFLOW**: +1. Create training configuration with proper policy, dataset, and environment settings +2. Start this learner server with the configuration +3. Start an actor server with the same configuration +4. Monitor training progress through wandb dashboard + +For more details on the complete HILSerl training workflow, see: +https://github.com/michel-aractingi/lerobot-hilserl-guide +""" + +import logging +import os +import shutil +import time +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +from pprint import pformat + +import grpc +import torch +from termcolor import colored +from torch import nn +from torch.multiprocessing import Queue +from torch.optim.optimizer import Optimizer + +from lerobot.common.cameras import opencv # noqa: F401 +from lerobot.common.constants import ( + CHECKPOINTS_DIR, + LAST_CHECKPOINT_LINK, + PRETRAINED_MODEL_DIR, + TRAINING_STATE_DIR, +) +from lerobot.common.datasets.factory import make_dataset +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.policies.factory import make_policy +from lerobot.common.policies.sac.modeling_sac import SACPolicy +from lerobot.common.robots import so100_follower # noqa: F401 +from lerobot.common.teleoperators import gamepad, so100_leader # noqa: F401 +from lerobot.common.transport import services_pb2_grpc +from lerobot.common.transport.utils import ( + bytes_to_python_object, + bytes_to_transitions, + state_to_bytes, +) +from lerobot.common.utils.buffer import ReplayBuffer, concatenate_batch_transitions +from lerobot.common.utils.process import ProcessSignalHandler +from lerobot.common.utils.random_utils import set_seed +from lerobot.common.utils.train_utils import ( + get_step_checkpoint_dir, + save_checkpoint, + update_last_checkpoint, +) +from lerobot.common.utils.train_utils import ( + load_training_state as utils_load_training_state, +) +from lerobot.common.utils.transition import move_state_dict_to_device, move_transition_to_device +from lerobot.common.utils.utils import ( + format_big_number, + get_safe_torch_device, + init_logging, +) +from lerobot.common.utils.wandb_utils import WandBLogger +from lerobot.configs import parser +from lerobot.configs.train import TrainRLServerPipelineConfig +from lerobot.scripts.rl import learner_service + +LOG_PREFIX = "[LEARNER]" + + +################################################# +# MAIN ENTRY POINTS AND CORE ALGORITHM FUNCTIONS # +################################################# + + +@parser.wrap() +def train_cli(cfg: TrainRLServerPipelineConfig): + if not use_threads(cfg): + import torch.multiprocessing as mp + + mp.set_start_method("spawn") + + # Use the job_name from the config + train( + cfg, + job_name=cfg.job_name, + ) + + logging.info("[LEARNER] train_cli finished") + + +def train(cfg: TrainRLServerPipelineConfig, job_name: str | None = None): + """ + Main training function that initializes and runs the training process. + + Args: + cfg (TrainRLServerPipelineConfig): The training configuration + job_name (str | None, optional): Job name for logging. Defaults to None. + """ + + cfg.validate() + + if job_name is None: + job_name = cfg.job_name + + if job_name is None: + raise ValueError("Job name must be specified either in config or as a parameter") + + display_pid = False + if not use_threads(cfg): + display_pid = True + + # Create logs directory to ensure it exists + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"learner_{job_name}.log") + + # Initialize logging with explicit log file + init_logging(log_file=log_file, display_pid=display_pid) + logging.info(f"Learner logging initialized, writing to {log_file}") + logging.info(pformat(cfg.to_dict())) + + # Setup WandB logging if enabled + if cfg.wandb.enable and cfg.wandb.project: + from lerobot.common.utils.wandb_utils import WandBLogger + + wandb_logger = WandBLogger(cfg) + else: + wandb_logger = None + logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"])) + + # Handle resume logic + cfg = handle_resume_logic(cfg) + + set_seed(seed=cfg.seed) + + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + + is_threaded = use_threads(cfg) + shutdown_event = ProcessSignalHandler(is_threaded, display_pid=display_pid).shutdown_event + + start_learner_threads( + cfg=cfg, + wandb_logger=wandb_logger, + shutdown_event=shutdown_event, + ) + + +def start_learner_threads( + cfg: TrainRLServerPipelineConfig, + wandb_logger: WandBLogger | None, + shutdown_event: any, # Event, +) -> None: + """ + Start the learner threads for training. + + Args: + cfg (TrainRLServerPipelineConfig): Training configuration + wandb_logger (WandBLogger | None): Logger for metrics + shutdown_event: Event to signal shutdown + """ + # Create multiprocessing queues + transition_queue = Queue() + interaction_message_queue = Queue() + parameters_queue = Queue() + + concurrency_entity = None + + if use_threads(cfg): + from threading import Thread + + concurrency_entity = Thread + else: + from torch.multiprocessing import Process + + concurrency_entity = Process + + communication_process = concurrency_entity( + target=start_learner, + args=( + parameters_queue, + transition_queue, + interaction_message_queue, + shutdown_event, + cfg, + ), + daemon=True, + ) + communication_process.start() + + add_actor_information_and_train( + cfg=cfg, + wandb_logger=wandb_logger, + shutdown_event=shutdown_event, + transition_queue=transition_queue, + interaction_message_queue=interaction_message_queue, + parameters_queue=parameters_queue, + ) + logging.info("[LEARNER] Training process stopped") + + logging.info("[LEARNER] Closing queues") + transition_queue.close() + interaction_message_queue.close() + parameters_queue.close() + + communication_process.join() + logging.info("[LEARNER] Communication process joined") + + logging.info("[LEARNER] join queues") + transition_queue.cancel_join_thread() + interaction_message_queue.cancel_join_thread() + parameters_queue.cancel_join_thread() + + logging.info("[LEARNER] queues closed") + + +################################################# +# Core algorithm functions # +################################################# + + +def add_actor_information_and_train( + cfg: TrainRLServerPipelineConfig, + wandb_logger: WandBLogger | None, + shutdown_event: any, # Event, + transition_queue: Queue, + interaction_message_queue: Queue, + parameters_queue: Queue, +): + """ + Handles data transfer from the actor to the learner, manages training updates, + and logs training progress in an online reinforcement learning setup. + + This function continuously: + - Transfers transitions from the actor to the replay buffer. + - Logs received interaction messages. + - Ensures training begins only when the replay buffer has a sufficient number of transitions. + - Samples batches from the replay buffer and performs multiple critic updates. + - Periodically updates the actor, critic, and temperature optimizers. + - Logs training statistics, including loss values and optimization frequency. + + NOTE: This function doesn't have a single responsibility, it should be split into multiple functions + in the future. The reason why we did that is the GIL in Python. It's super slow the performance + are divided by 200. So we need to have a single thread that does all the work. + + Args: + cfg (TrainRLServerPipelineConfig): Configuration object containing hyperparameters. + wandb_logger (WandBLogger | None): Logger for tracking training progress. + shutdown_event (Event): Event to signal shutdown. + transition_queue (Queue): Queue for receiving transitions from the actor. + interaction_message_queue (Queue): Queue for receiving interaction messages from the actor. + parameters_queue (Queue): Queue for sending policy parameters to the actor. + """ + # Extract all configuration variables at the beginning, it improve the speed performance + # of 7% + device = get_safe_torch_device(try_device=cfg.policy.device, log=True) + storage_device = get_safe_torch_device(try_device=cfg.policy.storage_device) + clip_grad_norm_value = cfg.policy.grad_clip_norm + online_step_before_learning = cfg.policy.online_step_before_learning + utd_ratio = cfg.policy.utd_ratio + fps = cfg.env.fps + log_freq = cfg.log_freq + save_freq = cfg.save_freq + policy_update_freq = cfg.policy.policy_update_freq + policy_parameters_push_frequency = cfg.policy.actor_learner_config.policy_parameters_push_frequency + saving_checkpoint = cfg.save_checkpoint + online_steps = cfg.policy.online_steps + async_prefetch = cfg.policy.async_prefetch + + # Initialize logging for multiprocessing + if not use_threads(cfg): + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"learner_train_process_{os.getpid()}.log") + init_logging(log_file=log_file, display_pid=True) + logging.info("Initialized logging for actor information and training process") + + logging.info("Initializing policy") + + policy: SACPolicy = make_policy( + cfg=cfg.policy, + env_cfg=cfg.env, + ) + + assert isinstance(policy, nn.Module) + + policy.train() + + push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy) + + last_time_policy_pushed = time.time() + + optimizers, lr_scheduler = make_optimizers_and_scheduler(cfg=cfg, policy=policy) + + # If we are resuming, we need to load the training state + resume_optimization_step, resume_interaction_step = load_training_state(cfg=cfg, optimizers=optimizers) + + log_training_info(cfg=cfg, policy=policy) + + replay_buffer = initialize_replay_buffer(cfg, device, storage_device) + batch_size = cfg.batch_size + offline_replay_buffer = None + + if cfg.dataset is not None: + offline_replay_buffer = initialize_offline_replay_buffer( + cfg=cfg, + device=device, + storage_device=storage_device, + ) + batch_size: int = batch_size // 2 # We will sample from both replay buffer + + logging.info("Starting learner thread") + interaction_message = None + optimization_step = resume_optimization_step if resume_optimization_step is not None else 0 + interaction_step_shift = resume_interaction_step if resume_interaction_step is not None else 0 + + dataset_repo_id = None + if cfg.dataset is not None: + dataset_repo_id = cfg.dataset.repo_id + + # Initialize iterators + online_iterator = None + offline_iterator = None + + # NOTE: THIS IS THE MAIN LOOP OF THE LEARNER + while True: + # Exit the training loop if shutdown is requested + if shutdown_event is not None and shutdown_event.is_set(): + logging.info("[LEARNER] Shutdown signal received. Exiting...") + break + + # Process all available transitions to the replay buffer, send by the actor server + process_transitions( + transition_queue=transition_queue, + replay_buffer=replay_buffer, + offline_replay_buffer=offline_replay_buffer, + device=device, + dataset_repo_id=dataset_repo_id, + shutdown_event=shutdown_event, + ) + + # Process all available interaction messages sent by the actor server + interaction_message = process_interaction_messages( + interaction_message_queue=interaction_message_queue, + interaction_step_shift=interaction_step_shift, + wandb_logger=wandb_logger, + shutdown_event=shutdown_event, + ) + + # Wait until the replay buffer has enough samples to start training + if len(replay_buffer) < online_step_before_learning: + continue + + if online_iterator is None: + online_iterator = replay_buffer.get_iterator( + batch_size=batch_size, async_prefetch=async_prefetch, queue_size=2 + ) + + if offline_replay_buffer is not None and offline_iterator is None: + offline_iterator = offline_replay_buffer.get_iterator( + batch_size=batch_size, async_prefetch=async_prefetch, queue_size=2 + ) + + time_for_one_optimization_step = time.time() + for _ in range(utd_ratio - 1): + # Sample from the iterators + batch = next(online_iterator) + + if dataset_repo_id is not None: + batch_offline = next(offline_iterator) + batch = concatenate_batch_transitions( + left_batch_transitions=batch, right_batch_transition=batch_offline + ) + + actions = batch["action"] + rewards = batch["reward"] + observations = batch["state"] + next_observations = batch["next_state"] + done = batch["done"] + check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations) + + observation_features, next_observation_features = get_observation_features( + policy=policy, observations=observations, next_observations=next_observations + ) + + # Create a batch dictionary with all required elements for the forward method + forward_batch = { + "action": actions, + "reward": rewards, + "state": observations, + "next_state": next_observations, + "done": done, + "observation_feature": observation_features, + "next_observation_feature": next_observation_features, + "complementary_info": batch["complementary_info"], + } + + # Use the forward method for critic loss + critic_output = policy.forward(forward_batch, model="critic") + + # Main critic optimization + loss_critic = critic_output["loss_critic"] + optimizers["critic"].zero_grad() + loss_critic.backward() + critic_grad_norm = torch.nn.utils.clip_grad_norm_( + parameters=policy.critic_ensemble.parameters(), max_norm=clip_grad_norm_value + ) + optimizers["critic"].step() + + # Discrete critic optimization (if available) + if policy.config.num_discrete_actions is not None: + discrete_critic_output = policy.forward(forward_batch, model="discrete_critic") + loss_discrete_critic = discrete_critic_output["loss_discrete_critic"] + optimizers["discrete_critic"].zero_grad() + loss_discrete_critic.backward() + discrete_critic_grad_norm = torch.nn.utils.clip_grad_norm_( + parameters=policy.discrete_critic.parameters(), max_norm=clip_grad_norm_value + ) + optimizers["discrete_critic"].step() + + # Update target networks (main and discrete) + policy.update_target_networks() + + # Sample for the last update in the UTD ratio + batch = next(online_iterator) + + if dataset_repo_id is not None: + batch_offline = next(offline_iterator) + batch = concatenate_batch_transitions( + left_batch_transitions=batch, right_batch_transition=batch_offline + ) + + actions = batch["action"] + rewards = batch["reward"] + observations = batch["state"] + next_observations = batch["next_state"] + done = batch["done"] + + check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations) + + observation_features, next_observation_features = get_observation_features( + policy=policy, observations=observations, next_observations=next_observations + ) + + # Create a batch dictionary with all required elements for the forward method + forward_batch = { + "action": actions, + "reward": rewards, + "state": observations, + "next_state": next_observations, + "done": done, + "observation_feature": observation_features, + "next_observation_feature": next_observation_features, + } + + critic_output = policy.forward(forward_batch, model="critic") + + loss_critic = critic_output["loss_critic"] + optimizers["critic"].zero_grad() + loss_critic.backward() + critic_grad_norm = torch.nn.utils.clip_grad_norm_( + parameters=policy.critic_ensemble.parameters(), max_norm=clip_grad_norm_value + ).item() + optimizers["critic"].step() + + # Initialize training info dictionary + training_infos = { + "loss_critic": loss_critic.item(), + "critic_grad_norm": critic_grad_norm, + } + + # Discrete critic optimization (if available) + if policy.config.num_discrete_actions is not None: + discrete_critic_output = policy.forward(forward_batch, model="discrete_critic") + loss_discrete_critic = discrete_critic_output["loss_discrete_critic"] + optimizers["discrete_critic"].zero_grad() + loss_discrete_critic.backward() + discrete_critic_grad_norm = torch.nn.utils.clip_grad_norm_( + parameters=policy.discrete_critic.parameters(), max_norm=clip_grad_norm_value + ).item() + optimizers["discrete_critic"].step() + + # Add discrete critic info to training info + training_infos["loss_discrete_critic"] = loss_discrete_critic.item() + training_infos["discrete_critic_grad_norm"] = discrete_critic_grad_norm + + # Actor and temperature optimization (at specified frequency) + if optimization_step % policy_update_freq == 0: + for _ in range(policy_update_freq): + # Actor optimization + actor_output = policy.forward(forward_batch, model="actor") + loss_actor = actor_output["loss_actor"] + optimizers["actor"].zero_grad() + loss_actor.backward() + actor_grad_norm = torch.nn.utils.clip_grad_norm_( + parameters=policy.actor.parameters(), max_norm=clip_grad_norm_value + ).item() + optimizers["actor"].step() + + # Add actor info to training info + training_infos["loss_actor"] = loss_actor.item() + training_infos["actor_grad_norm"] = actor_grad_norm + + # Temperature optimization + temperature_output = policy.forward(forward_batch, model="temperature") + loss_temperature = temperature_output["loss_temperature"] + optimizers["temperature"].zero_grad() + loss_temperature.backward() + temp_grad_norm = torch.nn.utils.clip_grad_norm_( + parameters=[policy.log_alpha], max_norm=clip_grad_norm_value + ).item() + optimizers["temperature"].step() + + # Add temperature info to training info + training_infos["loss_temperature"] = loss_temperature.item() + training_infos["temperature_grad_norm"] = temp_grad_norm + training_infos["temperature"] = policy.temperature + + # Update temperature + policy.update_temperature() + + # Push policy to actors if needed + if time.time() - last_time_policy_pushed > policy_parameters_push_frequency: + push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy) + last_time_policy_pushed = time.time() + + # Update target networks (main and discrete) + policy.update_target_networks() + + # Log training metrics at specified intervals + if optimization_step % log_freq == 0: + training_infos["replay_buffer_size"] = len(replay_buffer) + if offline_replay_buffer is not None: + training_infos["offline_replay_buffer_size"] = len(offline_replay_buffer) + training_infos["Optimization step"] = optimization_step + + # Log training metrics + if wandb_logger: + wandb_logger.log_dict(d=training_infos, mode="train", custom_step_key="Optimization step") + + # Calculate and log optimization frequency + time_for_one_optimization_step = time.time() - time_for_one_optimization_step + frequency_for_one_optimization_step = 1 / (time_for_one_optimization_step + 1e-9) + + logging.info(f"[LEARNER] Optimization frequency loop [Hz]: {frequency_for_one_optimization_step}") + + # Log optimization frequency + if wandb_logger: + wandb_logger.log_dict( + { + "Optimization frequency loop [Hz]": frequency_for_one_optimization_step, + "Optimization step": optimization_step, + }, + mode="train", + custom_step_key="Optimization step", + ) + + optimization_step += 1 + if optimization_step % log_freq == 0: + logging.info(f"[LEARNER] Number of optimization step: {optimization_step}") + + # Save checkpoint at specified intervals + if saving_checkpoint and (optimization_step % save_freq == 0 or optimization_step == online_steps): + save_training_checkpoint( + cfg=cfg, + optimization_step=optimization_step, + online_steps=online_steps, + interaction_message=interaction_message, + policy=policy, + optimizers=optimizers, + replay_buffer=replay_buffer, + offline_replay_buffer=offline_replay_buffer, + dataset_repo_id=dataset_repo_id, + fps=fps, + ) + + +def start_learner( + parameters_queue: Queue, + transition_queue: Queue, + interaction_message_queue: Queue, + shutdown_event: any, # Event, + cfg: TrainRLServerPipelineConfig, +): + """ + Start the learner server for training. + It will receive transitions and interaction messages from the actor server, + and send policy parameters to the actor server. + + Args: + parameters_queue: Queue for sending policy parameters to the actor + transition_queue: Queue for receiving transitions from the actor + interaction_message_queue: Queue for receiving interaction messages from the actor + shutdown_event: Event to signal shutdown + cfg: Training configuration + """ + if not use_threads(cfg): + # Create a process-specific log file + log_dir = os.path.join(cfg.output_dir, "logs") + os.makedirs(log_dir, exist_ok=True) + log_file = os.path.join(log_dir, f"learner_process_{os.getpid()}.log") + + # Initialize logging with explicit log file + init_logging(log_file=log_file, display_pid=True) + logging.info("Learner server process logging initialized") + + # Setup process handlers to handle shutdown signal + # But use shutdown event from the main process + # Return back for MP + # TODO: Check if its useful + _ = ProcessSignalHandler(False, display_pid=True) + + service = learner_service.LearnerService( + shutdown_event=shutdown_event, + parameters_queue=parameters_queue, + seconds_between_pushes=cfg.policy.actor_learner_config.policy_parameters_push_frequency, + transition_queue=transition_queue, + interaction_message_queue=interaction_message_queue, + queue_get_timeout=cfg.policy.actor_learner_config.queue_get_timeout, + ) + + server = grpc.server( + ThreadPoolExecutor(max_workers=learner_service.MAX_WORKERS), + options=[ + ("grpc.max_receive_message_length", learner_service.MAX_MESSAGE_SIZE), + ("grpc.max_send_message_length", learner_service.MAX_MESSAGE_SIZE), + ], + ) + + services_pb2_grpc.add_LearnerServiceServicer_to_server( + service, + server, + ) + + host = cfg.policy.actor_learner_config.learner_host + port = cfg.policy.actor_learner_config.learner_port + + server.add_insecure_port(f"{host}:{port}") + server.start() + logging.info("[LEARNER] gRPC server started") + + shutdown_event.wait() + logging.info("[LEARNER] Stopping gRPC server...") + server.stop(learner_service.SHUTDOWN_TIMEOUT) + logging.info("[LEARNER] gRPC server stopped") + + +def save_training_checkpoint( + cfg: TrainRLServerPipelineConfig, + optimization_step: int, + online_steps: int, + interaction_message: dict | None, + policy: nn.Module, + optimizers: dict[str, Optimizer], + replay_buffer: ReplayBuffer, + offline_replay_buffer: ReplayBuffer | None = None, + dataset_repo_id: str | None = None, + fps: int = 30, +) -> None: + """ + Save training checkpoint and associated data. + + This function performs the following steps: + 1. Creates a checkpoint directory with the current optimization step + 2. Saves the policy model, configuration, and optimizer states + 3. Saves the current interaction step for resuming training + 4. Updates the "last" checkpoint symlink to point to this checkpoint + 5. Saves the replay buffer as a dataset for later use + 6. If an offline replay buffer exists, saves it as a separate dataset + + Args: + cfg: Training configuration + optimization_step: Current optimization step + online_steps: Total number of online steps + interaction_message: Dictionary containing interaction information + policy: Policy model to save + optimizers: Dictionary of optimizers + replay_buffer: Replay buffer to save as dataset + offline_replay_buffer: Optional offline replay buffer to save + dataset_repo_id: Repository ID for dataset + fps: Frames per second for dataset + """ + logging.info(f"Checkpoint policy after step {optimization_step}") + _num_digits = max(6, len(str(online_steps))) + interaction_step = interaction_message["Interaction step"] if interaction_message is not None else 0 + + # Create checkpoint directory + checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, online_steps, optimization_step) + + # Save checkpoint + save_checkpoint( + checkpoint_dir=checkpoint_dir, + step=optimization_step, + cfg=cfg, + policy=policy, + optimizer=optimizers, + scheduler=None, + ) + + # Save interaction step manually + training_state_dir = os.path.join(checkpoint_dir, TRAINING_STATE_DIR) + os.makedirs(training_state_dir, exist_ok=True) + training_state = {"step": optimization_step, "interaction_step": interaction_step} + torch.save(training_state, os.path.join(training_state_dir, "training_state.pt")) + + # Update the "last" symlink + update_last_checkpoint(checkpoint_dir) + + # TODO : temporary save replay buffer here, remove later when on the robot + # We want to control this with the keyboard inputs + dataset_dir = os.path.join(cfg.output_dir, "dataset") + if os.path.exists(dataset_dir) and os.path.isdir(dataset_dir): + shutil.rmtree(dataset_dir) + + # Save dataset + # NOTE: Handle the case where the dataset repo id is not specified in the config + # eg. RL training without demonstrations data + repo_id_buffer_save = cfg.env.task if dataset_repo_id is None else dataset_repo_id + replay_buffer.to_lerobot_dataset(repo_id=repo_id_buffer_save, fps=fps, root=dataset_dir) + + if offline_replay_buffer is not None: + dataset_offline_dir = os.path.join(cfg.output_dir, "dataset_offline") + if os.path.exists(dataset_offline_dir) and os.path.isdir(dataset_offline_dir): + shutil.rmtree(dataset_offline_dir) + + offline_replay_buffer.to_lerobot_dataset( + cfg.dataset.repo_id, + fps=fps, + root=dataset_offline_dir, + ) + + logging.info("Resume training") + + +def make_optimizers_and_scheduler(cfg: TrainRLServerPipelineConfig, policy: nn.Module): + """ + Creates and returns optimizers for the actor, critic, and temperature components of a reinforcement learning policy. + + This function sets up Adam optimizers for: + - The **actor network**, ensuring that only relevant parameters are optimized. + - The **critic ensemble**, which evaluates the value function. + - The **temperature parameter**, which controls the entropy in soft actor-critic (SAC)-like methods. + + It also initializes a learning rate scheduler, though currently, it is set to `None`. + + NOTE: + - If the encoder is shared, its parameters are excluded from the actor's optimization process. + - The policy's log temperature (`log_alpha`) is wrapped in a list to ensure proper optimization as a standalone tensor. + + Args: + cfg: Configuration object containing hyperparameters. + policy (nn.Module): The policy model containing the actor, critic, and temperature components. + + Returns: + Tuple[Dict[str, torch.optim.Optimizer], Optional[torch.optim.lr_scheduler._LRScheduler]]: + A tuple containing: + - `optimizers`: A dictionary mapping component names ("actor", "critic", "temperature") to their respective Adam optimizers. + - `lr_scheduler`: Currently set to `None` but can be extended to support learning rate scheduling. + + """ + optimizer_actor = torch.optim.Adam( + params=[ + p + for n, p in policy.actor.named_parameters() + if not policy.config.shared_encoder or not n.startswith("encoder") + ], + lr=cfg.policy.actor_lr, + ) + optimizer_critic = torch.optim.Adam(params=policy.critic_ensemble.parameters(), lr=cfg.policy.critic_lr) + + if cfg.policy.num_discrete_actions is not None: + optimizer_discrete_critic = torch.optim.Adam( + params=policy.discrete_critic.parameters(), lr=cfg.policy.critic_lr + ) + optimizer_temperature = torch.optim.Adam(params=[policy.log_alpha], lr=cfg.policy.critic_lr) + lr_scheduler = None + optimizers = { + "actor": optimizer_actor, + "critic": optimizer_critic, + "temperature": optimizer_temperature, + } + if cfg.policy.num_discrete_actions is not None: + optimizers["discrete_critic"] = optimizer_discrete_critic + return optimizers, lr_scheduler + + +################################################# +# Training setup functions # +################################################# + + +def handle_resume_logic(cfg: TrainRLServerPipelineConfig) -> TrainRLServerPipelineConfig: + """ + Handle the resume logic for training. + + If resume is True: + - Verifies that a checkpoint exists + - Loads the checkpoint configuration + - Logs resumption details + - Returns the checkpoint configuration + + If resume is False: + - Checks if an output directory exists (to prevent accidental overwriting) + - Returns the original configuration + + Args: + cfg (TrainRLServerPipelineConfig): The training configuration + + Returns: + TrainRLServerPipelineConfig: The updated configuration + + Raises: + RuntimeError: If resume is True but no checkpoint found, or if resume is False but directory exists + """ + out_dir = cfg.output_dir + + # Case 1: Not resuming, but need to check if directory exists to prevent overwrites + if not cfg.resume: + checkpoint_dir = os.path.join(out_dir, CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK) + if os.path.exists(checkpoint_dir): + raise RuntimeError( + f"Output directory {checkpoint_dir} already exists. Use `resume=true` to resume training." + ) + return cfg + + # Case 2: Resuming training + checkpoint_dir = os.path.join(out_dir, CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK) + if not os.path.exists(checkpoint_dir): + raise RuntimeError(f"No model checkpoint found in {checkpoint_dir} for resume=True") + + # Log that we found a valid checkpoint and are resuming + logging.info( + colored( + "Valid checkpoint found: resume=True detected, resuming previous run", + color="yellow", + attrs=["bold"], + ) + ) + + # Load config using Draccus + checkpoint_cfg_path = os.path.join(checkpoint_dir, PRETRAINED_MODEL_DIR, "train_config.json") + checkpoint_cfg = TrainRLServerPipelineConfig.from_pretrained(checkpoint_cfg_path) + + # Ensure resume flag is set in returned config + checkpoint_cfg.resume = True + return checkpoint_cfg + + +def load_training_state( + cfg: TrainRLServerPipelineConfig, + optimizers: Optimizer | dict[str, Optimizer], +): + """ + Loads the training state (optimizers, step count, etc.) from a checkpoint. + + Args: + cfg (TrainRLServerPipelineConfig): Training configuration + optimizers (Optimizer | dict): Optimizers to load state into + + Returns: + tuple: (optimization_step, interaction_step) or (None, None) if not resuming + """ + if not cfg.resume: + return None, None + + # Construct path to the last checkpoint directory + checkpoint_dir = os.path.join(cfg.output_dir, CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK) + + logging.info(f"Loading training state from {checkpoint_dir}") + + try: + # Use the utility function from train_utils which loads the optimizer state + step, optimizers, _ = utils_load_training_state(Path(checkpoint_dir), optimizers, None) + + # Load interaction step separately from training_state.pt + training_state_path = os.path.join(checkpoint_dir, TRAINING_STATE_DIR, "training_state.pt") + interaction_step = 0 + if os.path.exists(training_state_path): + training_state = torch.load(training_state_path, weights_only=False) # nosec B614: Safe usage of torch.load + interaction_step = training_state.get("interaction_step", 0) + + logging.info(f"Resuming from step {step}, interaction step {interaction_step}") + return step, interaction_step + + except Exception as e: + logging.error(f"Failed to load training state: {e}") + return None, None + + +def log_training_info(cfg: TrainRLServerPipelineConfig, policy: nn.Module) -> None: + """ + Log information about the training process. + + Args: + cfg (TrainRLServerPipelineConfig): Training configuration + policy (nn.Module): Policy model + """ + num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad) + num_total_params = sum(p.numel() for p in policy.parameters()) + + logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}") + logging.info(f"{cfg.env.task=}") + logging.info(f"{cfg.policy.online_steps=}") + logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})") + logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})") + + +def initialize_replay_buffer( + cfg: TrainRLServerPipelineConfig, device: str, storage_device: str +) -> ReplayBuffer: + """ + Initialize a replay buffer, either empty or from a dataset if resuming. + + Args: + cfg (TrainRLServerPipelineConfig): Training configuration + device (str): Device to store tensors on + storage_device (str): Device for storage optimization + + Returns: + ReplayBuffer: Initialized replay buffer + """ + if not cfg.resume: + return ReplayBuffer( + capacity=cfg.policy.online_buffer_capacity, + device=device, + state_keys=cfg.policy.input_features.keys(), + storage_device=storage_device, + optimize_memory=True, + ) + + logging.info("Resume training load the online dataset") + dataset_path = os.path.join(cfg.output_dir, "dataset") + + # NOTE: In RL is possible to not have a dataset. + repo_id = None + if cfg.dataset is not None: + repo_id = cfg.dataset.repo_id + dataset = LeRobotDataset( + repo_id=repo_id, + root=dataset_path, + ) + return ReplayBuffer.from_lerobot_dataset( + lerobot_dataset=dataset, + capacity=cfg.policy.online_buffer_capacity, + device=device, + state_keys=cfg.policy.input_features.keys(), + optimize_memory=True, + ) + + +def initialize_offline_replay_buffer( + cfg: TrainRLServerPipelineConfig, + device: str, + storage_device: str, +) -> ReplayBuffer: + """ + Initialize an offline replay buffer from a dataset. + + Args: + cfg (TrainRLServerPipelineConfig): Training configuration + device (str): Device to store tensors on + storage_device (str): Device for storage optimization + + Returns: + ReplayBuffer: Initialized offline replay buffer + """ + if not cfg.resume: + logging.info("make_dataset offline buffer") + offline_dataset = make_dataset(cfg) + else: + logging.info("load offline dataset") + dataset_offline_path = os.path.join(cfg.output_dir, "dataset_offline") + offline_dataset = LeRobotDataset( + repo_id=cfg.dataset.repo_id, + root=dataset_offline_path, + ) + + logging.info("Convert to a offline replay buffer") + offline_replay_buffer = ReplayBuffer.from_lerobot_dataset( + offline_dataset, + device=device, + state_keys=cfg.policy.input_features.keys(), + storage_device=storage_device, + optimize_memory=True, + capacity=cfg.policy.offline_buffer_capacity, + ) + return offline_replay_buffer + + +################################################# +# Utilities/Helpers functions # +################################################# + + +def get_observation_features( + policy: SACPolicy, observations: torch.Tensor, next_observations: torch.Tensor +) -> tuple[torch.Tensor | None, torch.Tensor | None]: + """ + Get observation features from the policy encoder. It act as cache for the observation features. + when the encoder is frozen, the observation features are not updated. + We can save compute by caching the observation features. + + Args: + policy: The policy model + observations: The current observations + next_observations: The next observations + + Returns: + tuple: observation_features, next_observation_features + """ + + if policy.config.vision_encoder_name is None or not policy.config.freeze_vision_encoder: + return None, None + + with torch.no_grad(): + observation_features = policy.actor.encoder.get_cached_image_features(observations, normalize=True) + next_observation_features = policy.actor.encoder.get_cached_image_features( + next_observations, normalize=True + ) + + return observation_features, next_observation_features + + +def use_threads(cfg: TrainRLServerPipelineConfig) -> bool: + return cfg.policy.concurrency.learner == "threads" + + +def check_nan_in_transition( + observations: torch.Tensor, + actions: torch.Tensor, + next_state: torch.Tensor, + raise_error: bool = False, +) -> bool: + """ + Check for NaN values in transition data. + + Args: + observations: Dictionary of observation tensors + actions: Action tensor + next_state: Dictionary of next state tensors + raise_error: If True, raises ValueError when NaN is detected + + Returns: + bool: True if NaN values were detected, False otherwise + """ + nan_detected = False + + # Check observations + for key, tensor in observations.items(): + if torch.isnan(tensor).any(): + logging.error(f"observations[{key}] contains NaN values") + nan_detected = True + if raise_error: + raise ValueError(f"NaN detected in observations[{key}]") + + # Check next state + for key, tensor in next_state.items(): + if torch.isnan(tensor).any(): + logging.error(f"next_state[{key}] contains NaN values") + nan_detected = True + if raise_error: + raise ValueError(f"NaN detected in next_state[{key}]") + + # Check actions + if torch.isnan(actions).any(): + logging.error("actions contains NaN values") + nan_detected = True + if raise_error: + raise ValueError("NaN detected in actions") + + return nan_detected + + +def push_actor_policy_to_queue(parameters_queue: Queue, policy: nn.Module): + logging.debug("[LEARNER] Pushing actor policy to the queue") + state_dict = move_state_dict_to_device(policy.actor.state_dict(), device="cpu") + state_bytes = state_to_bytes(state_dict) + parameters_queue.put(state_bytes) + + +def process_interaction_message( + message, interaction_step_shift: int, wandb_logger: WandBLogger | None = None +): + """Process a single interaction message with consistent handling.""" + message = bytes_to_python_object(message) + # Shift interaction step for consistency with checkpointed state + message["Interaction step"] += interaction_step_shift + + # Log if logger available + if wandb_logger: + wandb_logger.log_dict(d=message, mode="train", custom_step_key="Interaction step") + + return message + + +def process_transitions( + transition_queue: Queue, + replay_buffer: ReplayBuffer, + offline_replay_buffer: ReplayBuffer, + device: str, + dataset_repo_id: str | None, + shutdown_event: any, +): + """Process all available transitions from the queue. + + Args: + transition_queue: Queue for receiving transitions from the actor + replay_buffer: Replay buffer to add transitions to + offline_replay_buffer: Offline replay buffer to add transitions to + device: Device to move transitions to + dataset_repo_id: Repository ID for dataset + shutdown_event: Event to signal shutdown + """ + while not transition_queue.empty() and not shutdown_event.is_set(): + transition_list = transition_queue.get() + transition_list = bytes_to_transitions(buffer=transition_list) + + for transition in transition_list: + transition = move_transition_to_device(transition=transition, device=device) + + # Skip transitions with NaN values + if check_nan_in_transition( + observations=transition["state"], + actions=transition["action"], + next_state=transition["next_state"], + ): + logging.warning("[LEARNER] NaN detected in transition, skipping") + continue + + replay_buffer.add(**transition) + + # Add to offline buffer if it's an intervention + if dataset_repo_id is not None and transition.get("complementary_info", {}).get( + "is_intervention" + ): + offline_replay_buffer.add(**transition) + + +def process_interaction_messages( + interaction_message_queue: Queue, + interaction_step_shift: int, + wandb_logger: WandBLogger | None, + shutdown_event: any, +) -> dict | None: + """Process all available interaction messages from the queue. + + Args: + interaction_message_queue: Queue for receiving interaction messages + interaction_step_shift: Amount to shift interaction step by + wandb_logger: Logger for tracking progress + shutdown_event: Event to signal shutdown + + Returns: + dict | None: The last interaction message processed, or None if none were processed + """ + last_message = None + while not interaction_message_queue.empty() and not shutdown_event.is_set(): + message = interaction_message_queue.get() + last_message = process_interaction_message( + message=message, + interaction_step_shift=interaction_step_shift, + wandb_logger=wandb_logger, + ) + + return last_message + + +if __name__ == "__main__": + train_cli() + logging.info("[LEARNER] main finished") diff --git a/lerobot/scripts/rl/learner_service.py b/lerobot/scripts/rl/learner_service.py new file mode 100644 index 0000000000000000000000000000000000000000..6480d6fd8c151456760209e0d75850ccaf319ed7 --- /dev/null +++ b/lerobot/scripts/rl/learner_service.py @@ -0,0 +1,118 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from multiprocessing import Event, Queue + +from lerobot.common.transport import services_pb2, services_pb2_grpc +from lerobot.common.transport.utils import receive_bytes_in_chunks, send_bytes_in_chunks +from lerobot.common.utils.queue import get_last_item_from_queue + +MAX_MESSAGE_SIZE = 4 * 1024 * 1024 # 4 MB +MAX_WORKERS = 3 # Stream parameters, send transitions and interactions +SHUTDOWN_TIMEOUT = 10 + + +class LearnerService(services_pb2_grpc.LearnerServiceServicer): + """ + Implementation of the LearnerService gRPC service + This service is used to send parameters to the Actor and receive transitions and interactions from the Actor + check transport.proto for the gRPC service definition + """ + + def __init__( + self, + shutdown_event: Event, # type: ignore + parameters_queue: Queue, + seconds_between_pushes: float, + transition_queue: Queue, + interaction_message_queue: Queue, + queue_get_timeout: float = 0.001, + ): + self.shutdown_event = shutdown_event + self.parameters_queue = parameters_queue + self.seconds_between_pushes = seconds_between_pushes + self.transition_queue = transition_queue + self.interaction_message_queue = interaction_message_queue + self.queue_get_timeout = queue_get_timeout + + def StreamParameters(self, request, context): # noqa: N802 + # TODO: authorize the request + logging.info("[LEARNER] Received request to stream parameters from the Actor") + + last_push_time = 0 + + while not self.shutdown_event.is_set(): + time_since_last_push = time.time() - last_push_time + if time_since_last_push < self.seconds_between_pushes: + self.shutdown_event.wait(self.seconds_between_pushes - time_since_last_push) + # Continue, because we could receive a shutdown event, + # and it's checked in the while loop + continue + + logging.info("[LEARNER] Push parameters to the Actor") + buffer = get_last_item_from_queue( + self.parameters_queue, block=True, timeout=self.queue_get_timeout + ) + + if buffer is None: + continue + + yield from send_bytes_in_chunks( + buffer, + services_pb2.Parameters, + log_prefix="[LEARNER] Sending parameters", + silent=True, + ) + + last_push_time = time.time() + logging.info("[LEARNER] Parameters sent") + + logging.info("[LEARNER] Stream parameters finished") + return services_pb2.Empty() + + def SendTransitions(self, request_iterator, _context): # noqa: N802 + # TODO: authorize the request + logging.info("[LEARNER] Received request to receive transitions from the Actor") + + receive_bytes_in_chunks( + request_iterator, + self.transition_queue, + self.shutdown_event, + log_prefix="[LEARNER] transitions", + ) + + logging.debug("[LEARNER] Finished receiving transitions") + return services_pb2.Empty() + + def SendInteractions(self, request_iterator, _context): # noqa: N802 + # TODO: authorize the request + logging.info("[LEARNER] Received request to receive interactions from the Actor") + + receive_bytes_in_chunks( + request_iterator, + self.interaction_message_queue, + self.shutdown_event, + log_prefix="[LEARNER] interactions", + ) + + logging.debug("[LEARNER] Finished receiving interactions") + return services_pb2.Empty() + + def Ready(self, request, context): # noqa: N802 + return services_pb2.Empty() diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..bb8ef907fec294fc4b4574a57c731da108b8e413 --- /dev/null +++ b/lerobot/scripts/train.py @@ -0,0 +1,288 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import time +from contextlib import nullcontext +from pprint import pformat +from typing import Any + +import torch +from termcolor import colored +from torch.amp import GradScaler +from torch.optim import Optimizer + +from lerobot.common.datasets.factory import make_dataset +from lerobot.common.datasets.sampler import EpisodeAwareSampler +from lerobot.common.datasets.utils import cycle +from lerobot.common.envs.factory import make_env +from lerobot.common.optim.factory import make_optimizer_and_scheduler +from lerobot.common.policies.factory import make_policy +from lerobot.common.policies.pretrained import PreTrainedPolicy +from lerobot.common.policies.utils import get_device_from_parameters +from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker +from lerobot.common.utils.random_utils import set_seed +from lerobot.common.utils.train_utils import ( + get_step_checkpoint_dir, + get_step_identifier, + load_training_state, + save_checkpoint, + update_last_checkpoint, +) +from lerobot.common.utils.utils import ( + format_big_number, + get_safe_torch_device, + has_method, + init_logging, +) +from lerobot.common.utils.wandb_utils import WandBLogger +from lerobot.configs import parser +from lerobot.configs.train import TrainPipelineConfig +from lerobot.scripts.eval import eval_policy + + +def update_policy( + train_metrics: MetricsTracker, + policy: PreTrainedPolicy, + batch: Any, + optimizer: Optimizer, + grad_clip_norm: float, + grad_scaler: GradScaler, + lr_scheduler=None, + use_amp: bool = False, + lock=None, +) -> tuple[MetricsTracker, dict]: + start_time = time.perf_counter() + device = get_device_from_parameters(policy) + policy.train() + with torch.autocast(device_type=device.type) if use_amp else nullcontext(): + loss, output_dict = policy.forward(batch) + # TODO(rcadene): policy.unnormalize_outputs(out_dict) + grad_scaler.scale(loss).backward() + + # Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**. + grad_scaler.unscale_(optimizer) + + grad_norm = torch.nn.utils.clip_grad_norm_( + policy.parameters(), + grad_clip_norm, + error_if_nonfinite=False, + ) + + # Optimizer's gradients are already unscaled, so scaler.step does not unscale them, + # although it still skips optimizer.step() if the gradients contain infs or NaNs. + with lock if lock is not None else nullcontext(): + grad_scaler.step(optimizer) + # Updates the scale for next iteration. + grad_scaler.update() + + optimizer.zero_grad() + + # Step through pytorch scheduler at every batch instead of epoch + if lr_scheduler is not None: + lr_scheduler.step() + + if has_method(policy, "update"): + # To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC). + policy.update() + + train_metrics.loss = loss.item() + train_metrics.grad_norm = grad_norm.item() + train_metrics.lr = optimizer.param_groups[0]["lr"] + train_metrics.update_s = time.perf_counter() - start_time + return train_metrics, output_dict + + +@parser.wrap() +def train(cfg: TrainPipelineConfig): + cfg.validate() + logging.info(pformat(cfg.to_dict())) + + if cfg.wandb.enable and cfg.wandb.project: + wandb_logger = WandBLogger(cfg) + else: + wandb_logger = None + logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"])) + + if cfg.seed is not None: + set_seed(cfg.seed) + + # Check device is available + device = get_safe_torch_device(cfg.policy.device, log=True) + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + + logging.info("Creating dataset") + dataset = make_dataset(cfg) + + # Create environment used for evaluating checkpoints during training on simulation data. + # On real-world data, no need to create an environment as evaluations are done outside train.py, + # using the eval.py instead, with gym_dora environment and dora-rs. + eval_env = None + if cfg.eval_freq > 0 and cfg.env is not None: + logging.info("Creating env") + eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs) + + logging.info("Creating policy") + policy = make_policy( + cfg=cfg.policy, + ds_meta=dataset.meta, + ) + + logging.info("Creating optimizer and scheduler") + optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy) + grad_scaler = GradScaler(device.type, enabled=cfg.policy.use_amp) + + step = 0 # number of policy updates (forward + backward + optim) + + if cfg.resume: + step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler) + + num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad) + num_total_params = sum(p.numel() for p in policy.parameters()) + + logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}") + if cfg.env is not None: + logging.info(f"{cfg.env.task=}") + logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})") + logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})") + logging.info(f"{dataset.num_episodes=}") + logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})") + logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})") + + # create dataloader for offline training + if hasattr(cfg.policy, "drop_n_last_frames"): + shuffle = False + sampler = EpisodeAwareSampler( + dataset.episode_data_index, + drop_n_last_frames=cfg.policy.drop_n_last_frames, + shuffle=True, + ) + else: + shuffle = True + sampler = None + + dataloader = torch.utils.data.DataLoader( + dataset, + num_workers=cfg.num_workers, + batch_size=cfg.batch_size, + shuffle=shuffle, + sampler=sampler, + pin_memory=device.type != "cpu", + drop_last=False, + ) + dl_iter = cycle(dataloader) + + policy.train() + + train_metrics = { + "loss": AverageMeter("loss", ":.3f"), + "grad_norm": AverageMeter("grdn", ":.3f"), + "lr": AverageMeter("lr", ":0.1e"), + "update_s": AverageMeter("updt_s", ":.3f"), + "dataloading_s": AverageMeter("data_s", ":.3f"), + } + + train_tracker = MetricsTracker( + cfg.batch_size, dataset.num_frames, dataset.num_episodes, train_metrics, initial_step=step + ) + + logging.info("Start offline training on a fixed dataset") + for _ in range(step, cfg.steps): + start_time = time.perf_counter() + batch = next(dl_iter) + train_tracker.dataloading_s = time.perf_counter() - start_time + + for key in batch: + if isinstance(batch[key], torch.Tensor): + batch[key] = batch[key].to(device, non_blocking=True) + + train_tracker, output_dict = update_policy( + train_tracker, + policy, + batch, + optimizer, + cfg.optimizer.grad_clip_norm, + grad_scaler=grad_scaler, + lr_scheduler=lr_scheduler, + use_amp=cfg.policy.use_amp, + ) + + # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we + # increment `step` here. + step += 1 + train_tracker.step() + is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 + is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps + is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0 + + if is_log_step: + logging.info(train_tracker) + if wandb_logger: + wandb_log_dict = train_tracker.to_dict() + if output_dict: + wandb_log_dict.update(output_dict) + wandb_logger.log_dict(wandb_log_dict, step) + train_tracker.reset_averages() + + if cfg.save_checkpoint and is_saving_step: + logging.info(f"Checkpoint policy after step {step}") + checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step) + save_checkpoint(checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler) + update_last_checkpoint(checkpoint_dir) + if wandb_logger: + wandb_logger.log_policy(checkpoint_dir) + + if cfg.env and is_eval_step: + step_id = get_step_identifier(step, cfg.steps) + logging.info(f"Eval policy at step {step}") + with ( + torch.no_grad(), + torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(), + ): + eval_info = eval_policy( + eval_env, + policy, + cfg.eval.n_episodes, + videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}", + max_episodes_rendered=4, + start_seed=cfg.seed, + ) + + eval_metrics = { + "avg_sum_reward": AverageMeter("∑rwrd", ":.3f"), + "pc_success": AverageMeter("success", ":.1f"), + "eval_s": AverageMeter("eval_s", ":.3f"), + } + eval_tracker = MetricsTracker( + cfg.batch_size, dataset.num_frames, dataset.num_episodes, eval_metrics, initial_step=step + ) + eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s") + eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward") + eval_tracker.pc_success = eval_info["aggregated"].pop("pc_success") + logging.info(eval_tracker) + if wandb_logger: + wandb_log_dict = {**eval_tracker.to_dict(), **eval_info} + wandb_logger.log_dict(wandb_log_dict, step, mode="eval") + wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval") + + if eval_env: + eval_env.close() + logging.info("End of training") + + +if __name__ == "__main__": + init_logging() + train() diff --git a/lerobot/scripts/visualize_dataset.py b/lerobot/scripts/visualize_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..11f9d60177d61cb3ba2505675d2967d3fd18c832 --- /dev/null +++ b/lerobot/scripts/visualize_dataset.py @@ -0,0 +1,292 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Visualize data of **all** frames of any episode of a dataset of type LeRobotDataset. + +Note: The last frame of the episode doesn't always correspond to a final state. +That's because our datasets are composed of transition from state to state up to +the antepenultimate state associated to the ultimate action to arrive in the final state. +However, there might not be a transition from a final state to another state. + +Note: This script aims to visualize the data used to train the neural networks. +~What you see is what you get~. When visualizing image modality, it is often expected to observe +lossy compression artifacts since these images have been decoded from compressed mp4 videos to +save disk space. The compression factor applied has been tuned to not affect success rate. + +Examples: + +- Visualize data stored on a local machine: +``` +local$ python lerobot/scripts/visualize_dataset.py \ + --repo-id lerobot/pusht \ + --episode-index 0 +``` + +- Visualize data stored on a distant machine with a local viewer: +``` +distant$ python lerobot/scripts/visualize_dataset.py \ + --repo-id lerobot/pusht \ + --episode-index 0 \ + --save 1 \ + --output-dir path/to/directory + +local$ scp distant:path/to/directory/lerobot_pusht_episode_0.rrd . +local$ rerun lerobot_pusht_episode_0.rrd +``` + +- Visualize data stored on a distant machine through streaming: +(You need to forward the websocket port to the distant machine, with +`ssh -L 9087:localhost:9087 username@remote-host`) +``` +distant$ python lerobot/scripts/visualize_dataset.py \ + --repo-id lerobot/pusht \ + --episode-index 0 \ + --mode distant \ + --ws-port 9087 + +local$ rerun ws://localhost:9087 +``` + +""" + +import argparse +import gc +import logging +import time +from pathlib import Path +from typing import Iterator + +import numpy as np +import rerun as rr +import torch +import torch.utils.data +import tqdm + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + + +class EpisodeSampler(torch.utils.data.Sampler): + def __init__(self, dataset: LeRobotDataset, episode_index: int): + from_idx = dataset.episode_data_index["from"][episode_index].item() + to_idx = dataset.episode_data_index["to"][episode_index].item() + self.frame_ids = range(from_idx, to_idx) + + def __iter__(self) -> Iterator: + return iter(self.frame_ids) + + def __len__(self) -> int: + return len(self.frame_ids) + + +def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray: + assert chw_float32_torch.dtype == torch.float32 + assert chw_float32_torch.ndim == 3 + c, h, w = chw_float32_torch.shape + assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}" + hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy() + return hwc_uint8_numpy + + +def visualize_dataset( + dataset: LeRobotDataset, + episode_index: int, + batch_size: int = 32, + num_workers: int = 0, + mode: str = "local", + web_port: int = 9090, + ws_port: int = 9087, + save: bool = False, + output_dir: Path | None = None, +) -> Path | None: + if save: + assert output_dir is not None, ( + "Set an output directory where to write .rrd files with `--output-dir path/to/directory`." + ) + + repo_id = dataset.repo_id + + logging.info("Loading dataloader") + episode_sampler = EpisodeSampler(dataset, episode_index) + dataloader = torch.utils.data.DataLoader( + dataset, + num_workers=num_workers, + batch_size=batch_size, + sampler=episode_sampler, + ) + + logging.info("Starting Rerun") + + if mode not in ["local", "distant"]: + raise ValueError(mode) + + spawn_local_viewer = mode == "local" and not save + rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer) + + # Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush + # when iterating on a dataloader with `num_workers` > 0 + # TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix + gc.collect() + + if mode == "distant": + rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port) + + logging.info("Logging to Rerun") + + for batch in tqdm.tqdm(dataloader, total=len(dataloader)): + # iterate over the batch + for i in range(len(batch["index"])): + rr.set_time_sequence("frame_index", batch["frame_index"][i].item()) + rr.set_time_seconds("timestamp", batch["timestamp"][i].item()) + + # display each camera image + for key in dataset.meta.camera_keys: + # TODO(rcadene): add `.compress()`? is it lossless? + rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i]))) + + # display each dimension of action space (e.g. actuators command) + if "action" in batch: + for dim_idx, val in enumerate(batch["action"][i]): + rr.log(f"action/{dim_idx}", rr.Scalar(val.item())) + + # display each dimension of observed state space (e.g. agent position in joint space) + if "observation.state" in batch: + for dim_idx, val in enumerate(batch["observation.state"][i]): + rr.log(f"state/{dim_idx}", rr.Scalar(val.item())) + + if "next.done" in batch: + rr.log("next.done", rr.Scalar(batch["next.done"][i].item())) + + if "next.reward" in batch: + rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item())) + + if "next.success" in batch: + rr.log("next.success", rr.Scalar(batch["next.success"][i].item())) + + if mode == "local" and save: + # save .rrd locally + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + repo_id_str = repo_id.replace("/", "_") + rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd" + rr.save(rrd_path) + return rrd_path + + elif mode == "distant": + # stop the process from exiting since it is serving the websocket connection + try: + while True: + time.sleep(1) + except KeyboardInterrupt: + print("Ctrl-C received. Exiting.") + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--repo-id", + type=str, + required=True, + help="Name of hugging face repository containing a LeRobotDataset dataset (e.g. `lerobot/pusht`).", + ) + parser.add_argument( + "--episode-index", + type=int, + required=True, + help="Episode to visualize.", + ) + parser.add_argument( + "--root", + type=Path, + default=None, + help="Root directory for the dataset stored locally (e.g. `--root data`). By default, the dataset will be loaded from hugging face cache folder, or downloaded from the hub if available.", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=None, + help="Directory path to write a .rrd file when `--save 1` is set.", + ) + parser.add_argument( + "--batch-size", + type=int, + default=32, + help="Batch size loaded by DataLoader.", + ) + parser.add_argument( + "--num-workers", + type=int, + default=4, + help="Number of processes of Dataloader for loading the data.", + ) + parser.add_argument( + "--mode", + type=str, + default="local", + help=( + "Mode of viewing between 'local' or 'distant'. " + "'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. " + "'distant' creates a server on the distant machine where the data is stored. " + "Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine." + ), + ) + parser.add_argument( + "--web-port", + type=int, + default=9090, + help="Web port for rerun.io when `--mode distant` is set.", + ) + parser.add_argument( + "--ws-port", + type=int, + default=9087, + help="Web socket port for rerun.io when `--mode distant` is set.", + ) + parser.add_argument( + "--save", + type=int, + default=0, + help=( + "Save a .rrd file in the directory provided by `--output-dir`. " + "It also deactivates the spawning of a viewer. " + "Visualize the data by running `rerun path/to/file.rrd` on your local machine." + ), + ) + + parser.add_argument( + "--tolerance-s", + type=float, + default=1e-4, + help=( + "Tolerance in seconds used to ensure data timestamps respect the dataset fps value" + "This is argument passed to the constructor of LeRobotDataset and maps to its tolerance_s constructor argument" + "If not given, defaults to 1e-4." + ), + ) + + args = parser.parse_args() + kwargs = vars(args) + repo_id = kwargs.pop("repo_id") + root = kwargs.pop("root") + tolerance_s = kwargs.pop("tolerance_s") + + logging.info("Loading dataset") + dataset = LeRobotDataset(repo_id, root=root, tolerance_s=tolerance_s) + + visualize_dataset(dataset, **vars(args)) + + +if __name__ == "__main__": + main() diff --git a/lerobot/scripts/visualize_dataset_html.py b/lerobot/scripts/visualize_dataset_html.py new file mode 100644 index 0000000000000000000000000000000000000000..01e81175e727cf3bccfc3816bb45a25e869a09a4 --- /dev/null +++ b/lerobot/scripts/visualize_dataset_html.py @@ -0,0 +1,482 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Visualize data of **all** frames of any episode of a dataset of type LeRobotDataset. + +Note: The last frame of the episode doesnt always correspond to a final state. +That's because our datasets are composed of transition from state to state up to +the antepenultimate state associated to the ultimate action to arrive in the final state. +However, there might not be a transition from a final state to another state. + +Note: This script aims to visualize the data used to train the neural networks. +~What you see is what you get~. When visualizing image modality, it is often expected to observe +lossly compression artifacts since these images have been decoded from compressed mp4 videos to +save disk space. The compression factor applied has been tuned to not affect success rate. + +Example of usage: + +- Visualize data stored on a local machine: +```bash +local$ python lerobot/scripts/visualize_dataset_html.py \ + --repo-id lerobot/pusht + +local$ open http://localhost:9090 +``` + +- Visualize data stored on a distant machine with a local viewer: +```bash +distant$ python lerobot/scripts/visualize_dataset_html.py \ + --repo-id lerobot/pusht + +local$ ssh -L 9090:localhost:9090 distant # create a ssh tunnel +local$ open http://localhost:9090 +``` + +- Select episodes to visualize: +```bash +python lerobot/scripts/visualize_dataset_html.py \ + --repo-id lerobot/pusht \ + --episodes 7 3 5 1 4 +``` +""" + +import argparse +import csv +import json +import logging +import re +import shutil +import tempfile +from io import StringIO +from pathlib import Path + +import numpy as np +import pandas as pd +import requests +from flask import Flask, redirect, render_template, request, url_for + +from lerobot import available_datasets +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.utils import IterableNamespace +from lerobot.common.utils.utils import init_logging + + +def run_server( + dataset: LeRobotDataset | IterableNamespace | None, + episodes: list[int] | None, + host: str, + port: str, + static_folder: Path, + template_folder: Path, +): + app = Flask(__name__, static_folder=static_folder.resolve(), template_folder=template_folder.resolve()) + app.config["SEND_FILE_MAX_AGE_DEFAULT"] = 0 # specifying not to cache + + @app.route("/") + def hommepage(dataset=dataset): + if dataset: + dataset_namespace, dataset_name = dataset.repo_id.split("/") + return redirect( + url_for( + "show_episode", + dataset_namespace=dataset_namespace, + dataset_name=dataset_name, + episode_id=0, + ) + ) + + dataset_param, episode_param = None, None + all_params = request.args + if "dataset" in all_params: + dataset_param = all_params["dataset"] + if "episode" in all_params: + episode_param = int(all_params["episode"]) + + if dataset_param: + dataset_namespace, dataset_name = dataset_param.split("/") + return redirect( + url_for( + "show_episode", + dataset_namespace=dataset_namespace, + dataset_name=dataset_name, + episode_id=episode_param if episode_param is not None else 0, + ) + ) + + featured_datasets = [ + "lerobot/aloha_static_cups_open", + "lerobot/columbia_cairlab_pusht_real", + "lerobot/taco_play", + ] + return render_template( + "visualize_dataset_homepage.html", + featured_datasets=featured_datasets, + lerobot_datasets=available_datasets, + ) + + @app.route("//") + def show_first_episode(dataset_namespace, dataset_name): + first_episode_id = 0 + return redirect( + url_for( + "show_episode", + dataset_namespace=dataset_namespace, + dataset_name=dataset_name, + episode_id=first_episode_id, + ) + ) + + @app.route("///episode_") + def show_episode(dataset_namespace, dataset_name, episode_id, dataset=dataset, episodes=episodes): + repo_id = f"{dataset_namespace}/{dataset_name}" + try: + if dataset is None: + dataset = get_dataset_info(repo_id) + except FileNotFoundError: + return ( + "Make sure to convert your LeRobotDataset to v2 & above. See how to convert your dataset at https://github.com/huggingface/lerobot/pull/461", + 400, + ) + dataset_version = ( + str(dataset.meta._version) if isinstance(dataset, LeRobotDataset) else dataset.codebase_version + ) + match = re.search(r"v(\d+)\.", dataset_version) + if match: + major_version = int(match.group(1)) + if major_version < 2: + return "Make sure to convert your LeRobotDataset to v2 & above." + + episode_data_csv_str, columns, ignored_columns = get_episode_data(dataset, episode_id) + dataset_info = { + "repo_id": f"{dataset_namespace}/{dataset_name}", + "num_samples": dataset.num_frames + if isinstance(dataset, LeRobotDataset) + else dataset.total_frames, + "num_episodes": dataset.num_episodes + if isinstance(dataset, LeRobotDataset) + else dataset.total_episodes, + "fps": dataset.fps, + } + if isinstance(dataset, LeRobotDataset): + video_paths = [ + dataset.meta.get_video_file_path(episode_id, key) for key in dataset.meta.video_keys + ] + videos_info = [ + { + "url": url_for("static", filename=str(video_path).replace("\\", "/")), + "filename": video_path.parent.name, + } + for video_path in video_paths + ] + tasks = dataset.meta.episodes[episode_id]["tasks"] + else: + video_keys = [key for key, ft in dataset.features.items() if ft["dtype"] == "video"] + videos_info = [ + { + "url": f"https://huggingface.co/datasets/{repo_id}/resolve/main/" + + dataset.video_path.format( + episode_chunk=int(episode_id) // dataset.chunks_size, + video_key=video_key, + episode_index=episode_id, + ), + "filename": video_key, + } + for video_key in video_keys + ] + + response = requests.get( + f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/episodes.jsonl", timeout=5 + ) + response.raise_for_status() + # Split into lines and parse each line as JSON + tasks_jsonl = [json.loads(line) for line in response.text.splitlines() if line.strip()] + + filtered_tasks_jsonl = [row for row in tasks_jsonl if row["episode_index"] == episode_id] + tasks = filtered_tasks_jsonl[0]["tasks"] + + videos_info[0]["language_instruction"] = tasks + + if episodes is None: + episodes = list( + range(dataset.num_episodes if isinstance(dataset, LeRobotDataset) else dataset.total_episodes) + ) + + return render_template( + "visualize_dataset_template.html", + episode_id=episode_id, + episodes=episodes, + dataset_info=dataset_info, + videos_info=videos_info, + episode_data_csv_str=episode_data_csv_str, + columns=columns, + ignored_columns=ignored_columns, + ) + + app.run(host=host, port=port) + + +def get_ep_csv_fname(episode_id: int): + ep_csv_fname = f"episode_{episode_id}.csv" + return ep_csv_fname + + +def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index): + """Get a csv str containing timeseries data of an episode (e.g. state and action). + This file will be loaded by Dygraph javascript to plot data in real time.""" + columns = [] + + selected_columns = [col for col, ft in dataset.features.items() if ft["dtype"] in ["float32", "int32"]] + selected_columns.remove("timestamp") + + ignored_columns = [] + for column_name in selected_columns: + shape = dataset.features[column_name]["shape"] + shape_dim = len(shape) + if shape_dim > 1: + selected_columns.remove(column_name) + ignored_columns.append(column_name) + + # init header of csv with state and action names + header = ["timestamp"] + + for column_name in selected_columns: + dim_state = ( + dataset.meta.shapes[column_name][0] + if isinstance(dataset, LeRobotDataset) + else dataset.features[column_name].shape[0] + ) + + if "names" in dataset.features[column_name] and dataset.features[column_name]["names"]: + column_names = dataset.features[column_name]["names"] + while not isinstance(column_names, list): + column_names = list(column_names.values())[0] + else: + column_names = [f"{column_name}_{i}" for i in range(dim_state)] + columns.append({"key": column_name, "value": column_names}) + + header += column_names + + selected_columns.insert(0, "timestamp") + + if isinstance(dataset, LeRobotDataset): + from_idx = dataset.episode_data_index["from"][episode_index] + to_idx = dataset.episode_data_index["to"][episode_index] + data = ( + dataset.hf_dataset.select(range(from_idx, to_idx)) + .select_columns(selected_columns) + .with_format("pandas") + ) + else: + repo_id = dataset.repo_id + + url = f"https://huggingface.co/datasets/{repo_id}/resolve/main/" + dataset.data_path.format( + episode_chunk=int(episode_index) // dataset.chunks_size, episode_index=episode_index + ) + df = pd.read_parquet(url) + data = df[selected_columns] # Select specific columns + + rows = np.hstack( + ( + np.expand_dims(data["timestamp"], axis=1), + *[np.vstack(data[col]) for col in selected_columns[1:]], + ) + ).tolist() + + # Convert data to CSV string + csv_buffer = StringIO() + csv_writer = csv.writer(csv_buffer) + # Write header + csv_writer.writerow(header) + # Write data rows + csv_writer.writerows(rows) + csv_string = csv_buffer.getvalue() + + return csv_string, columns, ignored_columns + + +def get_episode_video_paths(dataset: LeRobotDataset, ep_index: int) -> list[str]: + # get first frame of episode (hack to get video_path of the episode) + first_frame_idx = dataset.episode_data_index["from"][ep_index].item() + return [ + dataset.hf_dataset.select_columns(key)[first_frame_idx][key]["path"] + for key in dataset.meta.video_keys + ] + + +def get_episode_language_instruction(dataset: LeRobotDataset, ep_index: int) -> list[str]: + # check if the dataset has language instructions + if "language_instruction" not in dataset.features: + return None + + # get first frame index + first_frame_idx = dataset.episode_data_index["from"][ep_index].item() + + language_instruction = dataset.hf_dataset[first_frame_idx]["language_instruction"] + # TODO (michel-aractingi) hack to get the sentence, some strings in openx are badly stored + # with the tf.tensor appearing in the string + return language_instruction.removeprefix("tf.Tensor(b'").removesuffix("', shape=(), dtype=string)") + + +def get_dataset_info(repo_id: str) -> IterableNamespace: + response = requests.get( + f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/info.json", timeout=5 + ) + response.raise_for_status() # Raises an HTTPError for bad responses + dataset_info = response.json() + dataset_info["repo_id"] = repo_id + return IterableNamespace(dataset_info) + + +def visualize_dataset_html( + dataset: LeRobotDataset | None, + episodes: list[int] | None = None, + output_dir: Path | None = None, + serve: bool = True, + host: str = "127.0.0.1", + port: int = 9090, + force_override: bool = False, +) -> Path | None: + init_logging() + + template_dir = Path(__file__).resolve().parent.parent / "templates" + + if output_dir is None: + # Create a temporary directory that will be automatically cleaned up + output_dir = tempfile.mkdtemp(prefix="lerobot_visualize_dataset_") + + output_dir = Path(output_dir) + if output_dir.exists(): + if force_override: + shutil.rmtree(output_dir) + else: + logging.info(f"Output directory already exists. Loading from it: '{output_dir}'") + + output_dir.mkdir(parents=True, exist_ok=True) + + static_dir = output_dir / "static" + static_dir.mkdir(parents=True, exist_ok=True) + + if dataset is None: + if serve: + run_server( + dataset=None, + episodes=None, + host=host, + port=port, + static_folder=static_dir, + template_folder=template_dir, + ) + else: + # Create a simlink from the dataset video folder containing mp4 files to the output directory + # so that the http server can get access to the mp4 files. + if isinstance(dataset, LeRobotDataset): + ln_videos_dir = static_dir / "videos" + if not ln_videos_dir.exists(): + ln_videos_dir.symlink_to((dataset.root / "videos").resolve().as_posix()) + + if serve: + run_server(dataset, episodes, host, port, static_dir, template_dir) + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--repo-id", + type=str, + default=None, + help="Name of hugging face repositery containing a LeRobotDataset dataset (e.g. `lerobot/pusht` for https://huggingface.co/datasets/lerobot/pusht).", + ) + parser.add_argument( + "--root", + type=Path, + default=None, + help="Root directory for a dataset stored locally (e.g. `--root data`). By default, the dataset will be loaded from hugging face cache folder, or downloaded from the hub if available.", + ) + parser.add_argument( + "--load-from-hf-hub", + type=int, + default=0, + help="Load videos and parquet files from HF Hub rather than local system.", + ) + parser.add_argument( + "--episodes", + type=int, + nargs="*", + default=None, + help="Episode indices to visualize (e.g. `0 1 5 6` to load episodes of index 0, 1, 5 and 6). By default loads all episodes.", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=None, + help="Directory path to write html files and kickoff a web server. By default write them to 'outputs/visualize_dataset/REPO_ID'.", + ) + parser.add_argument( + "--serve", + type=int, + default=1, + help="Launch web server.", + ) + parser.add_argument( + "--host", + type=str, + default="127.0.0.1", + help="Web host used by the http server.", + ) + parser.add_argument( + "--port", + type=int, + default=9090, + help="Web port used by the http server.", + ) + parser.add_argument( + "--force-override", + type=int, + default=0, + help="Delete the output directory if it exists already.", + ) + + parser.add_argument( + "--tolerance-s", + type=float, + default=1e-4, + help=( + "Tolerance in seconds used to ensure data timestamps respect the dataset fps value" + "This is argument passed to the constructor of LeRobotDataset and maps to its tolerance_s constructor argument" + "If not given, defaults to 1e-4." + ), + ) + + args = parser.parse_args() + kwargs = vars(args) + repo_id = kwargs.pop("repo_id") + load_from_hf_hub = kwargs.pop("load_from_hf_hub") + root = kwargs.pop("root") + tolerance_s = kwargs.pop("tolerance_s") + + dataset = None + if repo_id: + dataset = ( + LeRobotDataset(repo_id, root=root, tolerance_s=tolerance_s) + if not load_from_hf_hub + else get_dataset_info(repo_id) + ) + + visualize_dataset_html(dataset, **vars(args)) + + +if __name__ == "__main__": + main() diff --git a/lerobot/scripts/visualize_image_transforms.py b/lerobot/scripts/visualize_image_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..16125ee641a15be8ddae92adde54091daf29aa1d --- /dev/null +++ b/lerobot/scripts/visualize_image_transforms.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Visualize effects of image transforms for a given configuration. + +This script will generate examples of transformed images as they are output by LeRobot dataset. +Additionally, each individual transform can be visualized separately as well as examples of combined transforms + +Example: +```bash +python lerobot/scripts/visualize_image_transforms.py \ + --repo_id=lerobot/pusht \ + --episodes='[0]' \ + --image_transforms.enable=True +``` +""" + +import logging +from copy import deepcopy +from dataclasses import replace +from pathlib import Path + +import draccus +from torchvision.transforms import ToPILImage + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.transforms import ( + ImageTransforms, + ImageTransformsConfig, + make_transform_from_config, +) +from lerobot.configs.default import DatasetConfig + +OUTPUT_DIR = Path("outputs/image_transforms") +to_pil = ToPILImage() + + +def save_all_transforms(cfg: ImageTransformsConfig, original_frame, output_dir, n_examples): + output_dir_all = output_dir / "all" + output_dir_all.mkdir(parents=True, exist_ok=True) + + tfs = ImageTransforms(cfg) + for i in range(1, n_examples + 1): + transformed_frame = tfs(original_frame) + to_pil(transformed_frame).save(output_dir_all / f"{i}.png", quality=100) + + print("Combined transforms examples saved to:") + print(f" {output_dir_all}") + + +def save_each_transform(cfg: ImageTransformsConfig, original_frame, output_dir, n_examples): + if not cfg.enable: + logging.warning( + "No single transforms will be saved, because `image_transforms.enable=False`. To enable, set `enable` to True in `ImageTransformsConfig` or in the command line with `--image_transforms.enable=True`." + ) + return + + print("Individual transforms examples saved to:") + for tf_name, tf_cfg in cfg.tfs.items(): + # Apply a few transformation with random value in min_max range + output_dir_single = output_dir / tf_name + output_dir_single.mkdir(parents=True, exist_ok=True) + + tf = make_transform_from_config(tf_cfg) + for i in range(1, n_examples + 1): + transformed_frame = tf(original_frame) + to_pil(transformed_frame).save(output_dir_single / f"{i}.png", quality=100) + + # Apply min, max, average transformations + tf_cfg_kwgs_min = deepcopy(tf_cfg.kwargs) + tf_cfg_kwgs_max = deepcopy(tf_cfg.kwargs) + tf_cfg_kwgs_avg = deepcopy(tf_cfg.kwargs) + + for key, (min_, max_) in tf_cfg.kwargs.items(): + avg = (min_ + max_) / 2 + tf_cfg_kwgs_min[key] = [min_, min_] + tf_cfg_kwgs_max[key] = [max_, max_] + tf_cfg_kwgs_avg[key] = [avg, avg] + + tf_min = make_transform_from_config(replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_min})) + tf_max = make_transform_from_config(replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_max})) + tf_avg = make_transform_from_config(replace(tf_cfg, **{"kwargs": tf_cfg_kwgs_avg})) + + tf_frame_min = tf_min(original_frame) + tf_frame_max = tf_max(original_frame) + tf_frame_avg = tf_avg(original_frame) + + to_pil(tf_frame_min).save(output_dir_single / "min.png", quality=100) + to_pil(tf_frame_max).save(output_dir_single / "max.png", quality=100) + to_pil(tf_frame_avg).save(output_dir_single / "mean.png", quality=100) + + print(f" {output_dir_single}") + + +@draccus.wrap() +def visualize_image_transforms(cfg: DatasetConfig, output_dir: Path = OUTPUT_DIR, n_examples: int = 5): + dataset = LeRobotDataset( + repo_id=cfg.repo_id, + episodes=cfg.episodes, + revision=cfg.revision, + video_backend=cfg.video_backend, + ) + + output_dir = output_dir / cfg.repo_id.split("/")[-1] + output_dir.mkdir(parents=True, exist_ok=True) + + # Get 1st frame from 1st camera of 1st episode + original_frame = dataset[0][dataset.meta.camera_keys[0]] + to_pil(original_frame).save(output_dir / "original_frame.png", quality=100) + print("\nOriginal frame saved to:") + print(f" {output_dir / 'original_frame.png'}.") + + save_all_transforms(cfg.image_transforms, original_frame, output_dir, n_examples) + save_each_transform(cfg.image_transforms, original_frame, output_dir, n_examples) + + +if __name__ == "__main__": + visualize_image_transforms() diff --git a/lerobot/setup_motors.py b/lerobot/setup_motors.py new file mode 100644 index 0000000000000000000000000000000000000000..ef16ca3ef194b6cd1411f7fcaadd681d18bcce8a --- /dev/null +++ b/lerobot/setup_motors.py @@ -0,0 +1,84 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Helper to set motor ids and baudrate. + +Example: + +```shell +python -m lerobot.setup_motors \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem575E0031751 +``` +""" + +from dataclasses import dataclass + +import draccus + +from .common.robots import ( # noqa: F401 + RobotConfig, + koch_follower, + lekiwi, + make_robot_from_config, + so100_follower, + so101_follower, +) +from .common.teleoperators import ( # noqa: F401 + TeleoperatorConfig, + koch_leader, + make_teleoperator_from_config, + so100_leader, + so101_leader, +) + +COMPATIBLE_DEVICES = [ + "koch_follower", + "koch_leader", + "so100_follower", + "so100_leader", + "so101_follower", + "so101_leader", + "lekiwi", +] + + +@dataclass +class SetupConfig: + teleop: TeleoperatorConfig | None = None + robot: RobotConfig | None = None + + def __post_init__(self): + if bool(self.teleop) == bool(self.robot): + raise ValueError("Choose either a teleop or a robot.") + + self.device = self.robot if self.robot else self.teleop + + +@draccus.wrap() +def setup_motors(cfg: SetupConfig): + if cfg.device.type not in COMPATIBLE_DEVICES: + raise NotImplementedError + + if isinstance(cfg.device, RobotConfig): + device = make_robot_from_config(cfg.device) + else: + device = make_teleoperator_from_config(cfg.device) + + device.setup_motors() + + +if __name__ == "__main__": + setup_motors() diff --git a/lerobot/teleoperate.py b/lerobot/teleoperate.py new file mode 100644 index 0000000000000000000000000000000000000000..3983dc143790d1a6a9636d0598cf4d02826f0041 --- /dev/null +++ b/lerobot/teleoperate.py @@ -0,0 +1,137 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Simple script to control a robot from teleoperation. + +Example: + +```shell +python -m lerobot.teleoperate \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ + --robot.id=black \ + --teleop.type=so101_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue \ + --display_data=true +``` +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pprint import pformat + +import draccus +import numpy as np +import rerun as rr + +from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 +from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 +from lerobot.common.robots import ( # noqa: F401 + Robot, + RobotConfig, + koch_follower, + make_robot_from_config, + so100_follower, + so101_follower, +) +from lerobot.common.teleoperators import ( + Teleoperator, + TeleoperatorConfig, + make_teleoperator_from_config, +) +from lerobot.common.utils.robot_utils import busy_wait +from lerobot.common.utils.utils import init_logging, move_cursor_up +from lerobot.common.utils.visualization_utils import _init_rerun + +from .common.teleoperators import gamepad, koch_leader, so100_leader, so101_leader # noqa: F401 + + +@dataclass +class TeleoperateConfig: + teleop: TeleoperatorConfig + robot: RobotConfig + # Limit the maximum frames per second. + fps: int = 60 + teleop_time_s: float | None = None + # Display all cameras on screen + display_data: bool = False + + +def teleop_loop( + teleop: Teleoperator, robot: Robot, fps: int, display_data: bool = False, duration: float | None = None +): + display_len = max(len(key) for key in robot.action_features) + start = time.perf_counter() + while True: + loop_start = time.perf_counter() + action = teleop.get_action() + if display_data: + observation = robot.get_observation() + for obs, val in observation.items(): + if isinstance(val, float): + rr.log(f"observation_{obs}", rr.Scalar(val)) + elif isinstance(val, np.ndarray): + rr.log(f"observation_{obs}", rr.Image(val), static=True) + for act, val in action.items(): + if isinstance(val, float): + rr.log(f"action_{act}", rr.Scalar(val)) + + robot.send_action(action) + dt_s = time.perf_counter() - loop_start + busy_wait(1 / fps - dt_s) + + loop_s = time.perf_counter() - loop_start + + print("\n" + "-" * (display_len + 10)) + print(f"{'NAME':<{display_len}} | {'NORM':>7}") + for motor, value in action.items(): + print(f"{motor:<{display_len}} | {value:>7.2f}") + print(f"\ntime: {loop_s * 1e3:.2f}ms ({1 / loop_s:.0f} Hz)") + + if duration is not None and time.perf_counter() - start >= duration: + return + + move_cursor_up(len(action) + 5) + + +@draccus.wrap() +def teleoperate(cfg: TeleoperateConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + if cfg.display_data: + _init_rerun(session_name="teleoperation") + + teleop = make_teleoperator_from_config(cfg.teleop) + robot = make_robot_from_config(cfg.robot) + + teleop.connect() + robot.connect() + + try: + teleop_loop(teleop, robot, cfg.fps, display_data=cfg.display_data, duration=cfg.teleop_time_s) + except KeyboardInterrupt: + pass + finally: + if cfg.display_data: + rr.rerun_shutdown() + teleop.disconnect() + robot.disconnect() + + +if __name__ == "__main__": + teleoperate() diff --git a/lerobot/templates/visualize_dataset_homepage.html b/lerobot/templates/visualize_dataset_homepage.html new file mode 100644 index 0000000000000000000000000000000000000000..c93aa826b86f99893967d4775199089e1e45833a --- /dev/null +++ b/lerobot/templates/visualize_dataset_homepage.html @@ -0,0 +1,68 @@ + + + + + + Interactive Video Background Page + + + + +
+ +
+
+
+
+

LeRobot Dataset Visualizer

+ + create & train your own robots + +

+
+

Example Datasets:

+
    + {% for dataset in featured_datasets %} +
  • {{ dataset }}
    • + {% endfor %} +
+
+
+
+ + +
+ +
+ More example datasets +
    + {% for dataset in lerobot_datasets %} +
  • {{ dataset }}
    • + {% endfor %} +
+
+
+ + diff --git a/lerobot/templates/visualize_dataset_template.html b/lerobot/templates/visualize_dataset_template.html new file mode 100644 index 0000000000000000000000000000000000000000..a99a224283e92de5b8679db7db90e3eddae4ab40 --- /dev/null +++ b/lerobot/templates/visualize_dataset_template.html @@ -0,0 +1,546 @@ + + + + + + + + + + + {{ dataset_info.repo_id }} episode {{ episode_id }} + + + + + + + +
+ + +

{{ dataset_info.repo_id }}

+ + +
    +
  • + Number of samples/frames: {{ dataset_info.num_samples }} +
    • +
  • + Number of episodes: {{ dataset_info.num_episodes }} +
    • +
  • + Frames per second: {{ dataset_info.fps }} +
    • +
+ +

Episodes:

+ + + + +
+ +
+ +
+ +
+ +
+ + + + + +
+

+ Episode {{ episode_id }} +

+ + + + + +
+
+ filter videos +
🔽
+
+ +
+
+ +
+
+
+ +
+ {% for video_info in videos_info %} +
+

{{ video_info.filename }}

+ +
+ {% endfor %} +
+ + + {% if videos_info[0].language_instruction %} +

+ Language Instruction: {{ videos_info[0].language_instruction }} +

+ {% endif %} + + + + + +
+ + + + + + +
0:00 / + 0:00 +
+
+ + +
+
+
+
+

+ Time: 0.00s +

+
+ +
+ + + + + + + + + + +
+ + + + {% if ignored_columns|length > 0 %} +
+ Columns {{ ignored_columns }} are NOT shown since the visualizer currently does not support 2D or 3D data. +
+ {% endif %} +
+ +
+
+ + + + + + + + + diff --git a/media/gym/aloha_act.gif b/media/gym/aloha_act.gif new file mode 100644 index 0000000000000000000000000000000000000000..18a7f3fe635760741f73561909be7f250a73677a --- /dev/null +++ b/media/gym/aloha_act.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f10f8a91a65c62a844a255c17dd30c820c1c805c13af00fb49c6499b7cbed849 +size 3027981 diff --git a/media/gym/pusht_diffusion.gif b/media/gym/pusht_diffusion.gif new file mode 100644 index 0000000000000000000000000000000000000000..2efff2a3b1ce7781a40ea247a7e4fcf2895b7e12 --- /dev/null +++ b/media/gym/pusht_diffusion.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4599f556ab12717698774d14781c1e5b9afbf1c57df0a9c6cc25ca2a3032fecc +size 189800 diff --git a/media/gym/simxarm_tdmpc.gif b/media/gym/simxarm_tdmpc.gif new file mode 100644 index 0000000000000000000000000000000000000000..191d9104f09857bfae198ec6482e2132af282f5b --- /dev/null +++ b/media/gym/simxarm_tdmpc.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11739ff20376cd0a3deee86b98b493f25b32ff03e975624044e2c3b80b35302c +size 475007 diff --git a/media/lekiwi/kiwi.webp b/media/lekiwi/kiwi.webp new file mode 100644 index 0000000000000000000000000000000000000000..1d5596cfd0c1d876897b959bb9eb9d075277274c --- /dev/null +++ b/media/lekiwi/kiwi.webp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e17d0417db3f5481437814e31b469546fe4e9ae7b8aa69b2180c97e1f3393d6c +size 224412 diff --git a/media/lerobot-logo-light.png b/media/lerobot-logo-light.png new file mode 100644 index 0000000000000000000000000000000000000000..c9b1011d256dfd138817722a4ba9edf9b71d3aac --- /dev/null +++ b/media/lerobot-logo-light.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5bf6cd4c35a017f0d498be07cd4d3f004ee3a023c43383abdeb13163a591be0b +size 204038 diff --git a/media/lerobot-logo-thumbnail.png b/media/lerobot-logo-thumbnail.png new file mode 100644 index 0000000000000000000000000000000000000000..d14daf6ada759f06fadc9684c4b5f5e01756d5ab --- /dev/null +++ b/media/lerobot-logo-thumbnail.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72ee48061c2528eb9f6a1f163622d4805476a52c813bd03f2f32e32d89afd63e +size 164066 diff --git a/media/so100/leader_follower.webp b/media/so100/leader_follower.webp new file mode 100644 index 0000000000000000000000000000000000000000..cbb688378cc5cf841f872242237304aefdcfc3f8 --- /dev/null +++ b/media/so100/leader_follower.webp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d62790391c7536c661cbd8eb2a4c5012d0fc44873f0811a75b7adea0c5d556a6 +size 120188 diff --git a/media/so101/so101-leader.webp b/media/so101/so101-leader.webp new file mode 100644 index 0000000000000000000000000000000000000000..29df0472c64769f9a464acaa4c73c23125affdec --- /dev/null +++ b/media/so101/so101-leader.webp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f60d1c14a0d322d077c5a239bf9ee3599a1488e7234706eed35f6986366961b +size 154650 diff --git a/media/so101/so101.webp b/media/so101/so101.webp new file mode 100644 index 0000000000000000000000000000000000000000..486f65bdb22b9adf6d651aa12d048ee0b1fa0a64 --- /dev/null +++ b/media/so101/so101.webp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:855809851ecf2ac5a28b2f0050b4baca3adc5a18c5175908399f9c6a52dd6877 +size 133522 diff --git a/media/wandb.png b/media/wandb.png new file mode 100644 index 0000000000000000000000000000000000000000..c0a834dfe1e0f2a36c58651f5348ac38daa11b83 --- /dev/null +++ b/media/wandb.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd6f74a6d07d26d4252246ed85c6c6719e0b8011c6f070e51d5521d7bb24dc67 +size 416489 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..cd0a4abe47a11bf73148f6b063e3d91ef3bf2ee1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,148 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +[project.urls] +homepage = "https://github.com/huggingface/lerobot" +issues = "https://github.com/huggingface/lerobot/issues" +discord = "https://discord.gg/s3KuuzsPFb" + +[project] +name = "lerobot" +version = "0.1.0" +description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch" +authors = [ + { name = "Rémi Cadène", email = "re.cadene@gmail.com" }, + { name = "Simon Alibert", email = "alibert.sim@gmail.com" }, + { name = "Alexander Soare", email = "alexander.soare159@gmail.com" }, + { name = "Quentin Gallouédec", email = "quentin.gallouedec@ec-lyon.fr" }, + { name = "Adil Zouitine", email = "adilzouitinegm@gmail.com" }, + { name = "Thomas Wolf", email = "thomaswolfcontact@gmail.com" }, + { name = "Steven Palma", email = "imstevenpmwork@ieee.org" }, +] +readme = "README.md" +license = { text = "Apache-2.0" } +requires-python = ">=3.10" +keywords = ["robotics", "deep learning", "pytorch"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "Topic :: Software Development :: Build Tools", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3.10", +] +dependencies = [ + "cmake>=3.29.0.1", + "datasets>=2.19.0", + "deepdiff>=7.0.1", + "diffusers>=0.27.2", + "draccus==0.10.0", + "einops>=0.8.0", + "flask>=3.0.3", + "gdown>=5.1.0", + "gymnasium==0.29.1", # TODO(rcadene, aliberts): Make gym 1.0.0 work + "h5py>=3.10.0", + "huggingface-hub[hf-transfer,cli]>=0.27.1 ; python_version < '4.0'", + "imageio[ffmpeg]>=2.34.0", + "jsonlines>=4.0.0", + "numba>=0.59.0", + "omegaconf>=2.3.0", + "opencv-python-headless>=4.9.0", + "packaging>=24.2", + "av>=14.2.0", + "pymunk>=6.6.0,<7.0.0", + "pynput>=1.7.7", + "pyserial>=3.5", + "pyzmq>=26.2.1", + "rerun-sdk>=0.21.0", + "scipy>=1.14.0", + "termcolor>=2.4.0", + "torch>=2.2.1", + "torchcodec>=0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", + "torchvision>=0.21.0", + "wandb>=0.16.3", + "zarr>=2.17.0", +] + +[project.optional-dependencies] +aloha = ["gym-aloha>=0.1.1 ; python_version < '4.0'"] +docs = ["hf-doc-builder @ git+https://github.com/huggingface/doc-builder.git@main", "watchdog >= 6.0.0"] +dev = ["pre-commit>=3.7.0", "debugpy>=1.8.1"] +dora = [ + "gym-dora @ git+https://github.com/dora-rs/dora-lerobot.git#subdirectory=gym_dora ; python_version < '4.0'", +] +dynamixel = ["dynamixel-sdk>=3.7.31"] +feetech = ["feetech-servo-sdk>=1.0.0"] +gamepad = ["pygame>=2.5.1", "hidapi>=0.14.0"] +intelrealsense = [ + "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'", + "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'", +] +pi0 = ["transformers>=4.48.0"] +smolvla = ["transformers>=4.50.3", "num2words>=0.5.14", "accelerate>=1.7.0", "safetensors>=0.4.3"] +pusht = ["gym-pusht>=0.1.5 ; python_version < '4.0'"] +stretch = [ + "hello-robot-stretch-body>=0.7.27 ; python_version < '4.0' and sys_platform == 'linux'", + "pyrender @ git+https://github.com/mmatl/pyrender.git ; sys_platform == 'linux'", + "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'" +] +test = ["pytest>=8.1.0", "pytest-timeout>=2.4.0", "pytest-cov>=5.0.0", "pyserial>=3.5", "mock-serial>=0.0.1 ; sys_platform != 'win32'"] +hilserl = ["transformers>=4.48", "gym-hil>=0.1.8", "protobuf>=5.29.3", "grpcio==1.71.0"] +umi = ["imagecodecs>=2024.1.1"] +video_benchmark = ["scikit-image>=0.23.2", "pandas>=2.2.2"] +xarm = ["gym-xarm>=0.1.1 ; python_version < '4.0'"] + +[tool.poetry] +requires-poetry = ">=2.1" + +[tool.ruff] +line-length = 110 +target-version = "py310" +exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"] + +[tool.ruff.lint] +select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401", "F403"] + +[tool.bandit] +exclude_dirs = [ + "tests", + "benchmarks", + "lerobot/common/datasets/push_dataset_to_hub", + "lerobot/common/datasets/v2/convert_dataset_v1_to_v2", + "lerobot/common/policies/pi0/conversion_scripts", + "lerobot/scripts/push_dataset_to_hub.py", +] +skips = ["B101", "B311", "B404", "B603"] + +[tool.typos] +default.extend-ignore-re = [ + "(?Rm)^.*(#|//)\\s*spellchecker:disable-line$", # spellchecker:disable-line + "(?s)(#|//)\\s*spellchecker:off.*?\\n\\s*(#|//)\\s*spellchecker:on", # spellchecker: +] +default.extend-ignore-identifiers-re = [ + # Add individual words here to ignore them + "2nd", + "pn", + "ser", + "ein", +] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..18159f1e30818efdb4f63a2f4010f968ddcc0daf --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License.