go2_rl_gym/README.md

Go2 RL GYM

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This repository builds on unitree_rl_gym to train the Unitree Go2 quadruped with reinforcement learning.

Isaac Gym	Mujoco	Physical

📦 Installation

Follow the step-by-step setup guide in setup.md.

🛠️ Usage Guide

1. Train

Run the following command to launch training:

python legged_gym/scripts/train.py --task=xxx --headless

⚙️ Arguments

• --task: Required. Options include go2, go2_cts, go2_moe_cts, go2_moe_ng_cts, go2_mcp_cts, go2_ac_moe_cts, go2_dual_moe_cts; go2_moe_cts is the paper's final version.
• --headless: Render viewer by default; set to true to disable rendering for higher throughput.
• --resume: Resume training from a chosen checkpoint in the logs.
• --experiment_name: Experiment folder to save/load from.
• --run_name: Run subfolder name to save/load from.
• --load_run: Name of the run to load (defaults to the most recent run).
• --checkpoint: Checkpoint index to load (defaults to the latest file).
• --num_envs: Number of parallel simulated environments.
• --seed: Random seed.
• --max_iterations: Maximum training iterations.
• --sim_device: Physics simulation device. Use --sim_device=cpu to force CPU.
• --rl_device: RL computation device. Use --rl_device=cpu to force CPU.
• --robogauge: Enable RoboGauge evaluation tool; disabled by default. Evaluation results are saved as results_{it}.yaml in logs/{exp_name}/{date}/robogauge_results and logged to TensorBoard.
• --robogauge_port: RoboGauge server port; default is 9973.

RoboGauge evaluation requires a separate server to be started. Refer to the RoboGauge documentation.

Default checkpoint path: logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt

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Model Evaluation

The trained model above was evaluated using the RoboGauge framework via Sim2Sim. The models in the table below are the best models after 150k training steps.

Model	Score	Tracking	Safety	Quality	Level	Download
go2_moe_cts (Ours)	0.6713	0.6669	0.7857	0.7392	7.85	ckpt
go2_ac_moe_cts	0.6509	0.6442	0.7644	0.7149	7.52	ckpt
go2_mcp_cts	0.6399	0.6355	0.7542	0.7058	7.41	ckpt
go2_moe_ng_cts	0.6519	0.6447	0.7639	0.7186	7.56	ckpt
CTS vanilla	0.5786	0.5755	0.7066	0.6624	6.83	ckpt
HIM	0.5379	0.5453	0.6476	0.6050	6.19	ckpt
DreamWaQ	0.5054	0.5105	0.6149	0.5730	5.74	ckpt

In the downloaded ckpt files, *.pt is used for Python deployment, and *.onnx is used for C++ deployment. The models above were all trained with self-collision disabled. In later tests, we found that enabling self-collision can also achieve strong results; see go2_moe_cts_self_0.6669 - ckpt.

2. Play

Visualize policies inside Gym with:

python legged_gym/scripts/play.py --task=xxx

Notes

• Play launches on randomized terrain with difficulty between 7 and 9.
• It automatically loads the latest checkpoint inside the experiment folder.
• You can specify another model via experiment_name, load_run, and checkpoint, for example:

  python legged_gym/scripts/play.py --task=go2_moe_cts --num_envs 100 --experiment_name go2_cts_hard_terrain --load_run Mar21_22-54-5-46_ --checkpoint 100000
  

💾 Policy Export

Play exports the Actor network to logs/{experiment_name}/exported/policies:

• policy.pt: TorchScript model for Sim2Sim.
• policy.onnx: ONNX model for Sim2Real.
• policy.pkl: Raw weights.

Demonstration

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3. Sim2Sim (Mujoco)

Run policies in the Mujoco simulator:

python deploy/deploy_mujoco/deploy_go2.py

Connect an Xbox-compatible gamepad to enable teleoperation; otherwise, the agent keeps a default forward command.

• Swap the policy: The default checkpoint is deploy/pre_train/go2/go2_cts_150k.pt. Replace policy_path in the YAML config with your own logs/{experiment_name}/exported/policies/policy.pt.
• Swap terrains: Default terrain is resources/robots/go2/stairs.xml. Alternatives include flat.xml, race_track.xml, cross_stairs.xml, and cross_slope.xml. Generate new terrains with windigal - mujoco_terrains

Results

Flat	Stairs	Race Track

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4. Sim2Real

4.1 Python Deployment

# 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 → Service, disable mcf/*, and enable the ota_box service.

Assuming the interface to the low-level controller is eth0:

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.

Demonstration

Python Deploy	C++ Deploy

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🎉 Acknowledgements

This repository would not exist without the following open-source projects:

• unitree_rl_gym: Unitree's core RL training framework.
• legged_gym: Base locomotion environment.
• rsl_rl: Reinforcement learning algorithms.
• mujoco: High-performance CPU physics simulator.
• unitree_sdk2_python: Python hardware interface for deployment.
• unitree_sdk2: C++ hardware interface for deployment.

Related publications implemented in this repo:

• CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion

Contributors:

• @windigal: CTS algorithm reproduction, terrain generation, video editing
• @wertyuilife2: CTS algorithm reproduction

---

📄 Citation

If you find our work helpful, please cite:

@article{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},
      year={2026},
      journal={arXiv preprint arXiv:2602.00678},
      url={https://arxiv.org/abs/2602.00678}, 
}

🔖 License

New contributions follow the MIT License; the original unitree_rl_gym remains under the BSD 3-Clause License.

See the complete LICENSE file for details.