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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

Install pytorch 1.10 with cuda-11.3:

pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

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
Install rsl_rl (PPO implementation)
- Clone https://github.com/leggedrobotics/rsl_rl
- cd rsl_rl && git checkout v1.0.2 && pip install -e .
Install unitree_rl_gym
- Navigate to the folder unitree_rl_gym
- pip install -e .

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.
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.