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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: robotics |
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--- |
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# Model Card for TRANSIC Policies |
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<!-- Provide a quick summary of what the model is/does. --> |
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This modelcard is accompanied with the [CoRL 2024 paper](https://arxiv.org/abs/2405.10315) titled TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction. |
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It includes robot policies trained in the simulation and transferred to the real world for complex and contact-rich manipulation tasks. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model repository includes three parts, 1) teacher policies trained in simulation with reinforcement learning; 2) student policies distilled from successful trajectories generated by teacher policies; and 3) residual policies learned in the real world to augment simulation policies. |
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The first part can be found in the `rl` directory. We provide RL teacher policies for 8 different tasks. The second part can be found in the `student` directory. We provide 5 student policies corresponding to 5 skills used in assembling the square table from [FurnitureBench](https://github.com/clvrai/furniture-bench). The third part can be found in the `residual` directory. They augment those 5 simulation base policies (student policies). |
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- **Developed by:** [Yunfan Jiang](https://yunfanj.com/) |
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- **Model type:** [PyTorch Checkpoints](https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html) |
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- **License:** [MIT](LICENSE) |
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### Model Sources |
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- **Repositories:** [TRANSIC](https://github.com/transic-robot/transic), [TRANSIC-Envs](https://github.com/transic-robot/transic-envs) |
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- **Paper:** [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://arxiv.org/abs/2405.10315) |
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- **Demo:** Provided on Our [Website](https://transic-robot.github.io/) |
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## Uses & How to Get Started with the Model |
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Please see our [codebase](https://github.com/transic-robot/transic) for detailed usage. |
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## Training Details |
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### Training Data |
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We provide training data in our [🤗Hugging Face data repository](https://huggingface.co/datasets/transic-robot/data). |
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### Training Procedure |
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1. Teacher policies are first trained with reinforcement learning from scratch in simulation; |
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2. We then rollout teacher policies to generate successful trajectories; |
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3. These generated data are used to train student policies through behavior cloning. Student policies take point-cloud and proprioceptive observations and output joint actions. |
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4. We then deploy student policies on the real robot. A human operator monitors the execution, intervenes when necessary, and provides online correction through teleoperation. Such teleoperation data are collected. |
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5. We use collected correction data to learn residual policies, which then augment simulation policies for successful sim-to-real transfer. |
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## Evaluation |
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Policies are evaluated in simulation and the real world. We use task success rate as the metric. |
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## Citation |
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**BibTeX:** |
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``` |
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@inproceedings{jiang2024transic, |
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title = {TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction}, |
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author = {Yunfan Jiang and Chen Wang and Ruohan Zhang and Jiajun Wu and Li Fei-Fei}, |
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booktitle = {Conference on Robot Learning}, |
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year = {2024} |
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} |
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``` |
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## Model Card Contact |
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[Yunfan Jiang](https://yunfanj.com/), email: `yunfanj[at]cs[dot]stanford[dot]edu` |
