RealSourceData/RealSource-World

RealSource World

RealSource World is a large-scale real-world robotics manipulation dataset collected using the RS-02 dual-arm humanoid robot. This dataset contains diverse long-horizon manipulation tasks performed in real-world environments, with detailed annotations for atomic skills and quality assessments.

Key Features

• 14+ million frames of real-world dual-arm manipulation demonstrations.
• 11,428+ episodes across 29 distinct manipulation tasks.
• 57-dimensional proprioceptive state space including joint positions, velocities, forces, torques, and end-effector poses.
• Multi-camera visual observations (head camera, left hand camera, right hand camera) at 720x1280 resolution, 30 FPS.
• Fine-grained annotations with atomic skill segmentation and quality assessments for each episode.
• Diverse scenes including kitchen, conference room, convenience store, and household environments.
• Dual-arm coordination tasks demonstrating complex bimanual manipulation skills.

News

• [2025/11] RealSource World released on Hugging Face. Download Link

Update

Version History

Version 1.0 (November 2025)

• Initial Release
• Released RealSource World dataset on Hugging Face
• 29 manipulation tasks with 11,428 episodes
• 14+ million frames of real-world dual-arm manipulation demonstrations
• 57-dimensional proprioceptive state space
• Multi-camera visual observations (head, left hand, right hand cameras)
• Fine-grained annotations with atomic skill segmentation
• Complete camera parameters (intrinsic and extrinsic) for all episodes
• Quality assessments for each episode

Get Started

Download the Dataset

To download the full dataset, you can use the following code. If you encounter any issues, please refer to the official Hugging Face documentation.

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

# When prompted for a password, use an access token with read permissions.

Generate one from your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/RealSourceData/RealSource-world

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/RealSourceData/RealSource-world

If you only want to download a specific task from the RealSource World dataset, such as Arrange_the_cups, follow these steps:

# Ensure Git LFS is installed (https://git-lfs.com)
git lfs install

# Initialize an empty Git repository
git init RealSource-world
cd RealSource-world

# Set the remote repository
git remote add origin https://huggingface.co/datasets/RealSourceData/RealSource-world

# Enable sparse-checkout
git sparse-checkout init

# Specify the folders and files you want to download
git sparse-checkout set Arrange_the_cups scripts

# Pull the data from the main branch
git pull origin main

Dataset Structure

Folder Hierarchy

RealSource-world/
├── Arrange_the_cups/

# Task name (29 tasks in total)
│ ├── data/
│ │ └── chunk-000/
│ │ ├── episode_000000.parquet
│ │ ├── episode_000001.parquet
│ │ └── ...

## 871 parquet files for this task
│ ├── meta/
│ │ ├── info.json

# Dataset metadata and feature definitions
│ │ ├── episodes.jsonl

# Episode-level metadata
│ │ ├── episodes_stats.jsonl

# Episode statistics
│ │ ├── tasks.jsonl

# Task descriptions
│ │ ├── sub_tasks.jsonl

# Fine-grained sub-task annotations
│ │ └── camera.json

# Camera parameters for all episodes
│ └── videos/
│ └── chunk-000/
│ ├── observation.images.head_camera/
│ │ ├── episode_000000.mp4
│ │ └── ...
│ ├── observation.images.left_hand_camera/
│ │ ├── episode_000000.mp4
│ │ └── ...
│ └── observation.images.right_hand_camera/
│ ├── episode_000000.mp4
│ └── ...
├── Arrange_the_items_on_the_conference_table/
│ └── ...
├── Clean_the_convenience_store/
│ └── ...
└── ...

## 29 tasks in total

Understanding the Dataset Format

This dataset follows the LeRobot v2.1 format. Each task directory contains:

• data/: Parquet files storing time-series data (proprioceptive states, actions, timestamps)
• meta/: JSON/JSONL files with metadata, episode information, and annotations
• videos/: MP4 video files from three camera perspectives

Key Metadata Files

• meta/info.json: Contains dataset-level metadata including:

• Total episodes, frames, videos

• Feature definitions (action and observation shapes, names)

• Video specifications (resolution, codec, FPS)

• Robot type and codebase version

• meta/episodes.jsonl: One JSON object per line, each representing an episode with:

• episode_index: Episode identifier

• length: Number of frames in the episode

• tasks: List of task descriptions

• videos: Paths to video files for each camera

• meta/sub_tasks.jsonl: Fine-grained annotations for each episode, including:

• task_steps: List of atomic skill segments with start/end frames

• success_rating: Overall task success score (1-5)

• quality_assessments: Detailed quality metrics (PASS/FAIL/VALID)

• notes: Annotation metadata

• meta/camera.json: Camera intrinsic and extrinsic parameters for each episode

Loading and Using the Dataset

This dataset is compatible with the LeRobot library. Here's how to load and use it:

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

# Load a specific task
dataset_path = "RealSource-world/Arrange_the_cups"
repo_id = "RealSourceData/RealSource-world"

# Initialize the dataset
dataset = LeRobotDataset(dataset_path, repo_id=repo_id)

# Access episode data
episode_0 = dataset[0]

# First frame of first episode
episode_info = dataset.episode_data[0]

# Episode metadata

Iterate through episodes
for episode_idx in range(len(dataset.episode_data)):
 episode_length = dataset.episode_data[episode_idx]["length"]
 print(f"Episode {episode_idx} has {episode_length} frames")

# Visualize an episode
dataset.show_video(episode_idx=0, video_key="observation.images.head_camera")

Data Format Details

Proprioceptive State (57-dimensional)

The observation.state field contains comprehensive proprioceptive information:

Index Range	Components	Description
0-15	Joint positions	7 joints × 2 arms + 2 grippers = 16 DOF
16	Lift position	Mobile base lift height
17-22	Left arm force/torque	6D force (fx, fy, fz, mx, my, mz)
23-28	Right arm force/torque	6D force (fx, fy, fz, mx, my, mz)
29-35	Left joint velocities	7 joints = 7 DOF
36-42	Right joint velocities	7 joints = 7 DOF
43-49	Left end-effector pose	Position (x, y, z) + Quaternion (qw, qx, qy, qz)
50-56	Right end-effector pose	Position (x, y, z) + Quaternion (qw, qx, qy, qz)

State Field Names

[
 "LeftFollowerArm_Joint1.pos", ..., "LeftFollowerArm_Joint7.pos",
 "LeftGripper.pos",
 "RightFollowerArm_Joint1.pos", ..., "RightFollowerArm_Joint7.pos",
 "RightGripper.pos",
 "Lift.position",
 "LeftForce.fx", "LeftForce.fy", "LeftForce.fz",
 "LeftForce.mx", "LeftForce.my", "LeftForce.mz",
 "RightForce.fx", "RightForce.fy", "RightForce.fz",
 "RightForce.mx", "RightForce.my", "RightForce.mz",
 "LeftJoint_Vel1", ..., "LeftJoint_Vel7",
 "RightJoint_Vel1", ..., "RightJoint_Vel7",
 "LeftEnd_X", "LeftEnd_Y", "LeftEnd_Z",
 "LeftEnd_Qw", "LeftEnd_Qx", "LeftEnd_Qy", "LeftEnd_Qz",
 "RightEnd_X", "RightEnd_Y", "RightEnd_Z",
 "RightEnd_Qw", "RightEnd_Qx", "RightEnd_Qy", "RightEnd_Qz"
]

Action Space (17-dimensional)

The action field contains commands sent to the robot:

Components	Description
0-6	Left arm joint positions (7 DOF)
7	Left gripper position
8-14	Right arm joint positions (7 DOF)
15	Right gripper position
16	Lift command

Action Field Names

[
 "LeftLeaderArm_Joint1.pos", ..., "LeftLeaderArm_Joint7.pos",
 "LeftGripper.pos",
 "RightLeaderArm_Joint1.pos", ..., "RightLeaderArm_Joint7.pos",
 "RightGripper.pos",
 "Lift.command"
]

Visual Observations

Each episode includes synchronized video from three camera perspectives:

• observation.images.head_camera: Overhead/head-mounted view
• observation.images.left_hand_camera: Left end-effector mounted camera
• observation.images.right_hand_camera: Right end-effector mounted camera

Video Specifications:

• Resolution: 720 × 1280 pixels
• Frame rate: 30 FPS
• Codec: H.264
• Format: MP4

Camera Parameters

Each episode has corresponding camera parameters stored in meta/camera.json, keyed by episode_XXXXXX. The camera parameters include intrinsic parameters (camera matrix and distortion coefficients) and extrinsic parameters (hand-eye calibration).

File Structure

The camera.json file contains camera parameters for all episodes:

{
 "episode_000000": {
 "camera_ids": {
 "head": "245022300889",
 "left_arm": "245022301980",
 "right_arm": "245022300408",
 "foot": ""
 },
 "camera_parameters": {
 "head": {
 "720P": {
 "MTX": [[648.57, 0, 645.54], [0, 647.80, 375.38], [0, 0, 1]],
 "DIST": [-0.0513, 0.0587, -0.0006, 0.00096, -0.0186]
 },
 "480P": { ... }
 },
 "left_arm": { ... },
 "right_arm": { ... }
 },
 "hand_eye": {
 "left_arm_in_eye": {
 "R": [[...], [...], [...]],
 "T": [x, y, z]
 },
 "right_arm_in_eye": { ... },
 "left_arm_to_eye": { ... },
 "right_arm_to_eye": { ... }
 }
 },
 "episode_000001": { ... }
}

Camera Intrinsic Parameters

Each camera (head, left_arm, right_arm) has intrinsic parameters for two resolutions:

• MTX: 3×3 camera intrinsic matrix

[fx 0 cx]
[0 fy cy]
[0 0 1]

• fx, fy: Focal lengths in pixels

• cx, cy: Principal point (optical center) in pixels

• DIST: 5-element distortion coefficients (k1, k2, p1, p2, k3)

• Used for correcting radial and tangential distortion

Available Resolutions:

• 720P: Parameters for 720p video (720 × 1280)
• 480P: Parameters for 480p video (480 × 640)

Hand-Eye Calibration (Extrinsic Parameters)

The hand_eye section contains transformations between the robot end-effectors and cameras:

• left_arm_in_eye: Transformation from left end-effector camera to left arm end-effector center

• R: 3×3 rotation matrix

• T: 3×1 translation vector [x, y, z] in meters

• Represents the pose of the left wrist-mounted camera relative to the left arm end-effector center

• right_arm_in_eye: Transformation from right end-effector camera to right arm end-effector center

• Represents the pose of the right wrist-mounted camera relative to the right arm end-effector center

• left_arm_to_eye: Transformation from head camera to left arm base coordinate frame

• R: 3×3 rotation matrix

• T: 3×1 translation vector [x, y, z] in meters

• Represents the pose of the head camera relative to the left arm base frame

• right_arm_to_eye: Transformation from head camera to right arm base coordinate frame

• Represents the pose of the head camera relative to the right arm base frame

These parameters enable coordinate transformations between:

• Robot end-effector poses and camera image coordinates
• 3D positions in robot space and pixel coordinates in images
• Multi-view geometric operations and calibration
• Wrist camera frames and end-effector centers
• Head camera frame and arm base frames

Camera IDs

Each camera has a unique identifier:

• head: Head-mounted camera ID
• left_arm: Left end-effector camera ID
• right_arm: Right end-effector camera ID
• foot: Foot camera ID (if available)

Sub-task Annotations

Each episode in meta/sub_tasks.jsonl contains detailed annotations:

{
 "task": "Separate the two stacked cups in the dish and place them on the two sides of the dish.",
 "language": "zh",
 "task_index": 0,
 "episode_index": 0,
 "task_steps": [
 {
 "step_name": "Left arm picks up the stack of cups from the center of the plate",
 "start_frame": 100,
 "end_frame": 180,
 "description": "Left arm picks up the stack of cups from the center of the plate",
 "duration_frames": 80
 },
 ...
 ],
 "success_rating": 5,
 "notes": "annotation_date: 2025/11/13",
 "quality_assessments": {
 "overall_valid": "VALID",
 "movement_fluency": "PASS",
 "grasp_success": "PASS",
 "placement_quality": "PASS",
 ...
 },
 "total_frames": 946
}

Quality Assessment Metrics

• overall_valid: Overall episode validity (VALID/INVALID)
• movement_fluency: Smoothness of robot movements (PASS/FAIL)
• grasp_success: Success of grasping actions (PASS/FAIL)
• placement_quality: Quality of object placement (PASS/FAIL)
• no_drop: No objects were dropped during the task (PASS/FAIL)
• grasp_collisions: No collisions during grasping (PASS/FAIL)
• arm_collisions: No arm collisions (PASS/FAIL)
• operation_completeness: Task completion status (PASS/FAIL)
• And more...

Dataset Statistics

Overall Statistics

• Total Tasks: 29
• Total Episodes: 11,428
• Total Frames: 14,085,107
• Total Videos: 34,284 (3 cameras × 11,428 episodes)
• Robot Type: RS-02 (dual-arm humanoid robot)
• Dataset Format: LeRobot v2.1
• Video Resolution: 720 × 1280
• Frame Rate: 30 FPS

Task Distribution

The dataset includes diverse manipulation tasks across multiple domains:

• Kitchen Tasks: Arranging cups, cooking rice, steaming, cleaning counters
• Organization Tasks: Organizing magazines, tools, toys, glass tubes
• Household Tasks: Tiding up rooms, placing books, slippers
• Convenience Store Tasks: Cleaning store, organizing items
• Industrial Tasks: Moving parts between containers
• And more...

Robot URDF Model

The RealSource World dataset was collected using the RS-02 dual-arm humanoid robot. For simulation, visualization, and research purposes, we provide the URDF (Unified Robot Description Format) model of the RS-02 robot.

RS-02 Robot Specifications

• Robot Type: Dual-arm humanoid robot
• Total Links: 46 links
• Total Joints: 45 joints
• Arms: 2 × 7-DOF arms (left and right)
• End-effectors: Dual-arm grippers with 8 DOF each
• Base: Mobile platform with wheels and lift mechanism
• Sensors: Head camera, left/right hand cameras

URDF Package Structure

The RS-02 URDF package includes:

RS-02/
├── urdf/
│   ├── RS-02.urdf          # Main URDF file (59KB)
│   └── RS-02.csv           # Joint configuration data
├── meshes/                 # 3D mesh models (46 STL files)
│   ├── base_link.STL
│   ├── L_Link_1-7.STL      # Left arm links
│   ├── R_Link_1-7.STL      # Right arm links
│   ├── ltool_*.STL         # Left gripper components
│   ├── rtool_*.STL         # Right gripper components
│   ├── head_*.STL          # Head components
│   └── camera_*.STL        # Camera mounts
├── config/
│   └── joint_names_RS-02.yaml  # Joint name configuration
├── launch/
│   ├── display.launch      # RViz visualization
│   └── gazebo.launch       # Gazebo simulation
└── package.xml             # ROS package metadata

Using the URDF Model

For ROS/ROS2 Users

The URDF model can be used with ROS tools:

Visualization in RViz:

roslaunch RS-02 display.launch

Simulation in Gazebo:

roslaunch RS-02 gazebo.launch

License and Citation

All the data and code within this repo are under CC BY-NC-SA 4.0. Please consider citing our project if it helps your research.

@misc{realsourceworld,
 title={RealSource-World},
  author={RealSource},
 howpublished={\url{https://huggingface.co/datasets/RealSourceData/RealSource-world}},
 year={2025}
}