🦿 Legged Robotics in Genesis

A legged_gym based framework for training legged robots in genesis

Table of Contents

🦿 Legged Robotics in Genesis

📅 Updates

2025/03/22

I created a new repo based on legged_gym, containing custom implementation of some RL control papers.

2025/02/10

add measure_heights support, and provide a demo of exteroceptive locomotion (go2_rough)

2024/12/28

add wiki page for instructions

2024/12/26

add terrain support, optional terrain type: ["plane", "heightfield"].
move test results to tests.md

2024/12/24

add a new demo environment bipedal_walker

2024/12/23

divide main and deploy branches, deploy branch should be used with a custom rsl_rl(which will be open-source soon)

🌟 Features

Totally based on legged_gym

It's easy to use for those who are familiar with legged_gym and rsl_rl
Faster and Smaller

For a go2 walking on the plane task with 4096 envs, the training speed in Genesis is approximately 1.3x compared to Isaac Gym, while the graphics memory usage is roughly 1/2 compared to IsaacGym.

With this smaller memory usage, it's possible to run more parallel environments, which can further improve the training speed.

🧪 Test Results

For tests conducted on Genesis, please refer to tests.md

🛠 Installation

Create a new python virtual env with python>=3.10
Install PyTorch
Install Genesis following the instructions in the Genesis repo

Install rsl_rl and tensorboard

# Install rsl_rl.
git clone git@github.com:leggedrobotics/rsl_rl.git
cd rsl_rl && git checkout v1.0.2 && pip install -e . --use-pep517

# Install tensorboard.
pip install tensorboard

Install genesis_lr

git clone git@github.com:lupinjia/genesis_lr.git
cd genesis_lr
pip install -e .

👋 Usage

🚀 Quick Start

By default, the task is set to go2(in utils/helpers.py), we can run a training session with the following command:

cd legged_gym/scripts
python train.py --headless # run training without rendering

After the training is done, paste the run_name under logs/go2 to load_run in go2_config.py:

Then, run play.py to visualize the trained model:

📖 Instructions

For more detailed instructions, please refer to the wiki page

🖼️ Gallery

Go2	Bipedal Walker

🙏 Acknowledgements

TODO

Add domain randomization
Verify the trained model on real robots.
Add Heightfield support
Add meausre_heights support
Add go2 deploy demos and instructions (vanilla and explicit estimator)
Add teacher-student implementation

License

lupinjia/genesis_lr

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