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RL-Lite3

Note

This code repository is no longer maintained! Please use the new rl_training repo.

简体中文

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
# if your GPU is RTX40 series, use the command below
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
# if your GPU is older versions
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, recording to the README.txt in Isaac Gym folder, open the docs/install.html file with browser，follow the instructions to install isaac gym in current Python environment.

  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

Communication