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--- |
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license: mit |
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language: |
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- en |
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--- |
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# RDT-1B |
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RDT-1B is a 1B-parameter imitation learning Diffusion Transformer pre-trained on 1M+ multi-robot episodes. Given a language instruction and 3-view RGB image observations, RDT can predict the next |
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64 robot actions. RDT is inherently compatible with almost all kinds of modern mobile manipulators, from single-arm to dual-arm, joint to EEF, pos. to vel., and even with a mobile chassis. |
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All the [code]() and pretrained model weights are licensed under MIT license. |
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Please refer to our [project page](https://rdt-robotics.github.io/rdt-robotics/) and [paper]() for more information. |
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## Model Details |
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- **Developed by** RDT Team from Tsinghua University. |
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- **License:** MIT |
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- **Language(s) (NLP):** en |
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- **Model Architecture:** Diffusion Transformer. |
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- **Pretrain dataset:** Curated pretrain dataset collected from 46 datasets. Please see [here]() for detail. |
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- **Repository:** [repo_url] |
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- **Paper :** [paper_url] |
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- **Project Page:** https://rdt-robotics.github.io/rdt-robotics/ |
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## Uses |
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RDT takes language instruction, image observations and proprioception as input, and predicts the next 64 robot actions in the form of unified action space vector, |
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including all the main physical quantities of robots, including the end-effector and joint, position and velocity, base movement, etc. |
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### Getting Started |
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RDT-1B supports finetuning on custom dataset, deploying and inferencing on real-robots, as well as pretraining the model. |
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Please refer to [our repository](https://github.com/GeneralEmbodiedSystem/RoboticsDiffusionTransformer/blob/main/docs/pretrain.md) for all the above guides. |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
