This repository builds on unitree_rl_gym to train the Unitree Go2 quadruped with reinforcement learning.For the IsaacLab-based version, see go2_rl_robotlab.
|
Isaac Gym
|
Mujoco
|
Physical
|
|--- | --- | --- |
|  |  |  |
| 
| 
|
---
### 4. Sim2Real
#### 4.1 Python Deployment
```bash
# Onboard Jetson: pick Python by JetPack version
# JetPack 6: Python 3.10
# JetPack 5: Python 3.8
conda create -n deploy python=3.10
conda activate deploy
# Install the matching PyTorch wheel for your Jetson
# https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
pip3 install -e .
```
In the Unitree app, open Device β Service, disable `mcf/*`, and enable the `ota_box` service.
Assuming the interface to the low-level controller is `eth0`:
```bash
cd deploy/deploy_real
python deploy_real_go2.py eth0
```
Press `start` to stand and `A` to engage the controller.
#### 4.2 C++ Deployment
Follow the usage described in [unitree_cpp_deploy](https://github.com/wty-yy/unitree_cpp_deploy).
#### Demonstration
| Python Deploy | C++ Deploy |
| --- | --- |
|  |  |
C++ Deployment: Policy 1/2/4 trained by go2_rl_gym, Policy 3 trained by [go2_rl_robotlab](https://github.com/wertyuilife2/go2_rl_robotlab).
https://github.com/user-attachments/assets/b72e10f2-ffdb-407d-bb1f-9d545e7f9f63
---
## π Acknowledgements
This repository would not exist without the following open-source projects:
- [unitree_rl_gym](https://github.com/unitreerobotics/unitree_rl_gym): Unitree's core RL training framework.
- [legged_gym](https://github.com/leggedrobotics/legged_gym): Base locomotion environment.
- [rsl_rl](https://github.com/leggedrobotics/rsl_rl.git): Reinforcement learning algorithms.
- [mujoco](https://github.com/google-deepmind/mujoco.git): High-performance CPU physics simulator.
- [unitree_sdk2_python](https://github.com/unitreerobotics/unitree_sdk2_python.git): Python hardware interface for deployment.
- [unitree_sdk2](https://github.com/unitreerobotics/unitree_sdk2): C++ hardware interface for deployment.
Related publications implemented in this repo:
- [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://arxiv.org/pdf/2405.10830)
Contributors:
- [@windigal](https://github.com/windigal): CTS algorithm reproduction, terrain generation, video editing
- [@wertyuilife2](https://github.com/wertyuilife2): CTS algorithm reproduction
---
## π Citation
If you find our work helpful, please cite:
```bibtex
@inproceedings{wu2026robogauge,
title={Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion},
author={Tianyang Wu and Hanwei Guo and Yuhang Wang and Junshu Yang and Xinyang Sui and Jiayi Xie and Xingyu Chen and Zeyang Liu and Xuguang Lan},
booktitle={Proceedings of Robotics: Science and Systems},
year={2026}
}
```
## π License
New contributions follow the [MIT License](LICENSE); the original unitree_rl_gym remains under the [BSD 3-Clause License](LICENSE).
See the complete [LICENSE file](LICENSE) for details.