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Unitree RL GYM

This is a simple example of using Unitree Robots for reinforcement learning, including Unitree Go2, H1, H1_2, G1

1. Installation

1. Create a new python virtual env with python 3.8

2. Install pytorch 2.3.1 with cuda-12.1:

   pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
   

3. Install Isaac Gym

   • Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym
   • cd isaacgym/python && pip install -e .
   • Try running an example cd examples && python 1080_balls_of_solitude.py
   • For troubleshooting check docs isaacgym/docs/index.html

4. Install rsl_rl (PPO implementation)

   • Clone https://github.com/leggedrobotics/rsl_rl
   • cd rsl_rl && git checkout v1.0.2 && pip install -e .

5. Install unitree_rl_gym

   • Navigate to the folder unitree_rl_gym
   • pip install -e .

6. Install unitree_sdk2py (Optional for deploy on real robot)

   • Clone https://github.com/unitreerobotics/unitree_sdk2_python
   • cd unitree_sdk2_python & pip install -e .

2. Train in Isaac Gym

1. Train: python legged_gym/scripts/train.py --task=go2

   • To run on CPU add following arguments: --sim_device=cpu, --rl_device=cpu (sim on CPU and rl on GPU is possible).
   • To run headless (no rendering) add --headless.
   • Important : To improve performance, once the training starts press v to stop the rendering. You can then enable it later to check the progress.
   • The trained policy is saved in logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt. Where <experiment_name> and <run_name> are defined in the train config.
   • The following command line arguments override the values set in the config files:
   • --task TASK: Task name.
   • --resume: Resume training from a checkpoint
   • --experiment_name EXPERIMENT_NAME: Name of the experiment to run or load.
   • --run_name RUN_NAME: Name of the run.
   • --load_run LOAD_RUN: Name of the run to load when resume=True. If -1: will load the last run.
   • --checkpoint CHECKPOINT: Saved model checkpoint number. If -1: will load the last checkpoint.
   • --num_envs NUM_ENVS: Number of environments to create.
   • --seed SEED: Random seed.
   • --max_iterations MAX_ITERATIONS: Maximum number of training iterations.

2. Play:python legged_gym/scripts/play.py --task=go2

   • By default, the loaded policy is the last model of the last run of the experiment folder.
   • Other runs/model iteration can be selected by setting load_run and checkpoint in the train config.

2.1 Play Demo

Go2	G1	H1	H1_2

3. Sim in Mujoco

3.1 Mujoco Usage

To execute sim2sim in mujoco, execute the following command:

python deploy/deploy_mujoco/deploy_mujoco.py {config_name}

config_name: The file name of the configuration file. The configuration file will be found under deploy/deploy_mujoco/configs/, for example g1.yaml, h1.yaml, h1_2.yaml.

example:

python deploy/deploy_mujoco/deploy_mujoco.py g1.yaml

3.2 Mujoco Demo

G1	H1	H1_2

4. Deploy on Physical Robot

reference to Deploy on Physical Robot(English) | 实物部署（简体中文）