2.8 KiB
Installation Guide
System Requirements
- Operating System: Recommended Ubuntu 18.04 or later
- GPU: Nvidia GPU
- Driver Version: Recommended version 525 or later
1. Creating a Virtual Environment
It is recommended to run training or deployment programs in a virtual environment. Conda is recommended for creating virtual environments. If Conda is already installed on your system, you can skip step 1.1.
1.1 Download and Install MiniConda
MiniConda is a lightweight distribution of Conda, suitable for creating and managing virtual environments. Use the following commands to download and install:
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh
After installation, initialize Conda:
~/miniconda3/bin/conda init --all
source ~/.bashrc
1.2 Create a New Environment
Use the following command to create a virtual environment:
conda create -n unitree-rl python=3.8
1.3 Activate the Virtual Environment
conda activate unitree-rl
2. Installing Dependencies
2.1 Install PyTorch
PyTorch is a neural network computation framework used for model training and inference. Install it using the following command:
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
2.2 Install Isaac Gym
Isaac Gym is a rigid body simulation and training framework provided by Nvidia.
2.2.1 Download
Download Isaac Gym from Nvidia’s official website.
2.2.2 Install
After extracting the package, navigate to the isaacgym/python folder and install it using the following commands:
cd isaacgym/python
pip install -e .
2.2.3 Verify Installation
Run the following command. If a window opens displaying 1080 balls falling, the installation was successful:
cd examples
python 1080_balls_of_solitude.py
If you encounter any issues, refer to the official documentation at isaacgym/docs/index.html.
2.3 Install rsl_rl
rsl_rl is a library implementing reinforcement learning algorithms.
2.3.1 Install
cd rsl_rl
pip install -e .
2.4 Install go2_rl_gym
2.4.1 Download
Clone the repository using Git:
git clone https://github.com/unitreerobotics/go2_rl_gym.git
2.4.2 Install
Navigate to the directory and install it:
cd go2_rl_gym
pip install -e .
2.5 Install unitree_cpp_deploy (Optional)
Refer to our C++ deployment repository, which is based on unitree_rl_lab and specifically designed for deploying models trained in this repository: unitree_cpp_deploy.