RL-Lite3

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Introduction

A Learning-based locomotion controller for quadruped robots. It includes all components needed for training and hardware deployment on DeepRobotics Lite3.

Software architecture

This repository consists of below directories:

• rsl_rl: a package wrapping RL methods.
• legged_gym: gym-style environments of quadruped robots.

Prepare environment

1. Create a python (3.6/3.7/3.8, 3.8 recommended) environment on Ubuntu OS.

2. Install pytorch with cuda.

# pytorch
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. Download Isaac Gym (version >=preview 3) from the official website and put it into the root directory of the project.

4. Install python dependencies with pip.

pip3 install transformations matplotlib gym tensorboard numpy=1.23.5

5. Install legged_gym and rsl_rl by pip

cd legged_gym
pip install -e .

cd rsl_rl
pip install -e .

Usage

Train policy in the simulation

cd ${PROJECT_DIR}
python3 legged_gym/legged_gym/scripts/train.py --rl_device cuda:0 --sim_device cuda:0 --headless

Run controller in the simulation

cd ${PROJECT_DIR}
python3 legged_gym/legged_gym/scripts/play.py --rl_device cuda:0 --sim_device cuda:0 --load_run ${model_dir} --checkpoint ${model_name}

Check that your computer has a GPU, otherwise, replace the word cuda:0 with cpu. You should assign the path of the network model via --load_run and --checkpoint.

Run controller in the real-world

Copy your policy file to the project rl_deploy,then you can run your reinforcement learning controller in the real world

Reference

• legged_gym
• rsl_rl
• quadruped-robot

Communication