56 lines
2.7 KiB
Markdown
56 lines
2.7 KiB
Markdown
# Robot Parkour Learning #
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Project website: [https://robot-parkour.github.io/](https://robot-parkour.github.io/) <br>
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Authors:
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[Ziwen Zhuang*](https://ziwenzhuang.github.io/),
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[Zipeng Fu*](https://zipengfu.github.io/),
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[Jianren Wang](https://www.jianrenw.com),
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[Christopher Atkeson](http://www.cs.cmu.edu/~cga/),
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[Sören Schwertfeger](https://robotics.shanghaitech.edu.cn/people/soeren),
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[Chelsea Finn](https://ai.stanford.edu/~cbfinn/),
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[Hang Zhao](https://hangzhaomit.github.io/)<br>
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Conference on Robot Learning (CoRL) 2023, **Oral**, **Best Systems Paper Award Finalist (top 3)** <br>
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<p align="center">
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<img src="images/teaser.jpeg" width="80%"/>
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</p>
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## Repository Structure ##
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* `legged_gym`: contains the isaacgym environment and config files.
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- `legged_gym/legged_gym/envs/{robot}/`: contains all the training config files for a specific robot
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- `legged_gym/legged_gym/envs/base/`: contains all the environment implementation.
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- `legged_gym/legged_gym/utils/terrain/`: contains the terrain generation code.
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* `rsl_rl`: contains the network module and algorithm implementation. You can copy this folder directly to your robot.
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- `rsl_rl/rsl_rl/algorithms/`: contains the algorithm implementation.
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- `rsl_rl/rsl_rl/modules/`: contains the network module implementation.
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## Training in Simulation ##
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To install and run the code for training A1/Go2 in simulation, please clone this repository and follow the instructions in [legged_gym/README.md](legged_gym/README.md).
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## Hardware Deployment ##
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To deploy the trained model on your unitree Go1 robot, please follow the instructions in [Deploy-Go1.md](onboard_codes/Deploy-Go1.md) for deploying on the Unittree Go1 robot.
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To deploy the trained model on your unitree Go2 robot, please follow the instructions in [Deploy-Go2.md](onboard_codes/Deploy-Go2.md) for deploying on the Unittree Go2 robot.
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## Trouble Shooting ##
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If you cannot run the distillation part or all graphics computing goes to GPU 0 dispite you have multiple GPUs and have set the CUDA_VISIBLE_DEVICES, please use docker to isolate each GPU.
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## To Do ##
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- [x] Go1 training configuration (does not guarantee the same performance as the paper)
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- [ ] A1 deployment code
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- [x] Go1 deployment code
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- [x] Go2 training configuration example (does not guarantee the same performance as the paper)
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- [x] Go2 deployment code example
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## Citation ##
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If you find this project helpful to your research, please consider cite us! This is really important to us.
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```
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@inproceedings{
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zhuang2023robot,
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title={Robot Parkour Learning},
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author={Ziwen Zhuang and Zipeng Fu and Jianren Wang and Christopher G Atkeson and S{\"o}ren Schwertfeger and Chelsea Finn and Hang Zhao},
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booktitle={Conference on Robot Learning {CoRL}},
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year={2023}
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}
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```
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