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LICENSE

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+---
+title: MIT License
+spdx-id: MIT
+featured: true
+hidden: false
+
+description: A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
+
+how: Create a text file (typically named LICENSE or LICENSE.txt) in the root of your source code and copy the text of the license into the file. Replace [year] with the current year and [fullname] with the name (or names) of the copyright holders.
+
+using:
+  Babel: https://github.com/babel/babel/blob/master/LICENSE
+  .NET: https://github.com/dotnet/runtime/blob/main/LICENSE.TXT
+  Rails: https://github.com/rails/rails/blob/master/MIT-LICENSE
+
+permissions:
+  - commercial-use
+  - modifications
+  - distribution
+  - private-use
+
+conditions:
+  - include-copyright
+
+limitations:
+  - liability
+  - warranty
+
+---
+
+MIT License
+
+Copyright (c) 2024 Yunfan Jiang
+
+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.

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: robotics
+---
+# Model Card for TRANSIC Policies
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard is accompanied with the [paper](https://arxiv.org/abs/2405.10315) titled TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction.
+It includes robot policies trained in the simulation and transferred to the real world for complex and contact-rich manipulation tasks.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+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.
+
+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).
+
+
+- **Developed by:** [Yunfan Jiang](https://yunfanj.com/)
+- **Model type:** [PyTorch Checkpoints](https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html)
+- **License:** [MIT](LICENSE)
+
+### Model Sources
+
+- **Repositories:** [TRANSIC](https://github.com/transic-robot/transic), [TRANSIC-Envs](https://github.com/transic-robot/transic-envs)
+- **Paper:** [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://arxiv.org/abs/2405.10315)
+- **Demo:** Provided on Our [Website](https://transic-robot.github.io/)
+
+## Uses & How to Get Started with the Model
+
+Please see our [codebase](https://github.com/transic-robot/transic) for detailed usage.
+
+## Training Details
+
+### Training Data
+
+We provide training data in our [🤗Hugging Face data repository](https://huggingface.co/datasets/transic-robot/data).
+
+### Training Procedure
+
+1. Teacher policies are first trained with reinforcement learning from scratch in simulation;
+2. We then rollout teacher policies to generate successful trajectories;
+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.
+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.
+5. We use collected correction data to learn residual policies, which then augment simulation policies for successful sim-to-real transfer.
+
+## Evaluation
+
+Policies are evaluated in simulation and the real world. We use task success rate as the metric.
+
+## Citation
+
+**BibTeX:**
+
+```
+@article{jiang2024transic,
+  title   = {TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction},
+  author  = {Yunfan Jiang and Chen Wang and Ruohan Zhang and Jiajun Wu and Li Fei-Fei},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: Arxiv-2405.10315}
+}
+```
+
+## Model Card Contact
+
+[Yunfan Jiang](https://yunfanj.com/), email: `yunfanj[at]cs[dot]stanford[dot]edu`

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