Unitree RL GYM

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

Installation

1. Create a new python virtual env with python 3.6, 3.7 or 3.8 (3.8 recommended)

2. 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
   
   

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 .

Usage

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.

Robots Demo

1. Go2

https://github.com/user-attachments/assets/98395d82-d3f6-4548-b6ee-8edfce70ac3e

2. H1

https://github.com/user-attachments/assets/7762b4f9-1072-4794-8ef6-7dd253a7ad4c

3. H1-2

https://github.com/user-attachments/assets/695323a7-a2d9-445b-bda8-f1b697159c39

4. G1

https://github.com/user-attachments/assets/6063c03e-1143-4c75-8fda-793c8615cb08

mujoco(sim2sim)

1. H1

Execute the following command in the project path:

python deploy/deploy_mujoco/deploy_mujoco.py g1.yaml

Then you can get the following effect:

https://github.com/user-attachments/assets/10a84f8d-c02f-41cb-b2fd-76a97951b2c3

2. H1_2

Execute the following command in the project path:

python deploy/deploy_mujoco/deploy_mujoco.py h1_2.yaml

Then you can get the following effect:

https://github.com/user-attachments/assets/fdd4f53d-3235-4978-a77f-1c71b32fb301

3. G1

Execute the following command in the project path:

python deploy/deploy_mujoco/deploy_mujoco.py g1.yaml

Then you can get the following effect:

https://github.com/user-attachments/assets/99b892c3-7886-49f4-a7f1-0420b51443dd