3.8 KiB
Installation and Configuration Guide
System Requirements
- OS: Ubuntu 18.04 or higher is recommended
- GPU: Nvidia GPU
- Driver Version: Version 525 or higher is recommended
1. Create Virtual Environment
It is recommended to run training or deployment programs within a virtual environment. Conda is recommended for creating and managing 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 New Environment
Use the following command to create a virtual environment:
conda create -n unitree-rl python=3.8
1.3 Activate Virtual Environment
conda activate unitree-rl
2. Install 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 Nvidia's rigid body simulation and training framework.
2.2.1 Download
Download Isaac Gym from the Nvidia official website.
2.2.2 Install
Unzip the file, enter the isaacgym/python folder, and execute the following command to install:
cd isaacgym/python
pip install -e .
2.2.3 Verify Installation
Run the following commands. If a window pops up showing 1080 balls falling, the installation is successful:
cd examples
python 1080_balls_of_solitude.py
If there are any issues, please refer to the official documentation in isaacgym/docs/index.html.
2.3 Install rsl_rl
rsl_rl is a reinforcement learning algorithm library.
Our repository includes rsl_rl with new algorithms. Clone the Git repository:
git clone https://github.com/wty-yy/go2_rl_gym.git
2.3.1 Install
cd rsl_rl
pip install -e .
2.4 Install go2_rl_gym
Enter the directory and install:
cd go2_rl_gym
pip install -e .
2.5 Real Robot Deployment (Optional)
2.5.1 unitree_sdk2
C++ SDK. For compilation, please refer to the official tutorial.
2.5.2 unitree_sdk2_python (Choose for Python Deployment)
conda create -n kaiwu python=3.8
conda activate kaiwu
pip3 install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
cd unitree_sdk2_python
pip install -e .
2.5.3 Install unitree_cpp_deploy (Choose for C++ Deployment)
We use a modified C++ deployment repository based on unitree_rl_lab, specifically designed for deploying models trained in this repository. See unitree_cpp_deploy.
2.6 RoboGauge Evaluation (Optional)
RoboGauge is a project for evaluating quadruped robot performance via Sim2Sim in Mujoco. It performs asynchronous evaluation on the CPU during training. For specific details, refer to the README.
git clone [https://github.com/wty-yy/RoboGauge.git](https://github.com/wty-yy/RoboGauge.git)
cd RoboGauge
pip install -e .