144 lines
3.8 KiB
Markdown
144 lines
3.8 KiB
Markdown
# Installation and Configuration Guide
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## System Requirements
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- **OS**: Ubuntu 18.04 or higher is recommended
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- **GPU**: Nvidia GPU
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- **Driver Version**: Version 525 or higher is recommended
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---
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## 1. Create Virtual Environment
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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.
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### 1.1 Download and Install MiniConda
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MiniConda is a lightweight distribution of Conda suitable for creating and managing virtual environments. Use the following commands to download and install:
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```bash
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mkdir -p ~/miniconda3
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
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bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
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rm ~/miniconda3/miniconda.sh
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```
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After installation, initialize Conda:
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```bash
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~/miniconda3/bin/conda init --all
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source ~/.bashrc
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```
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### 1.2 Create New Environment
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Use the following command to create a virtual environment:
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```bash
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conda create -n unitree-rl python=3.8
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```
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### 1.3 Activate Virtual Environment
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```bash
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conda activate unitree-rl
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```
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---
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## 2. Install Dependencies
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### 2.1 Install PyTorch
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PyTorch is a neural network computation framework used for model training and inference. Install it using the following command:
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```bash
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conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
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```
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### 2.2 Install Isaac Gym
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Isaac Gym is Nvidia's rigid body simulation and training framework.
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#### 2.2.1 Download
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Download [Isaac Gym](https://developer.nvidia.com/isaac-gym) from the Nvidia official website.
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#### 2.2.2 Install
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Unzip the file, enter the `isaacgym/python` folder, and execute the following command to install:
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```bash
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cd isaacgym/python
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pip install -e .
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```
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#### 2.2.3 Verify Installation
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Run the following commands. If a window pops up showing 1080 balls falling, the installation is successful:
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```bash
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cd examples
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python 1080_balls_of_solitude.py
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```
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If there are any issues, please refer to the official documentation in `isaacgym/docs/index.html`.
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### 2.3 Install rsl_rl
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`rsl_rl` is a reinforcement learning algorithm library.
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Our repository includes `rsl_rl` with new algorithms. Clone the Git repository:
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```bash
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git clone https://github.com/wty-yy/go2_rl_gym.git
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```
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#### 2.3.1 Install
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```bash
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cd rsl_rl
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pip install -e .
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```
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### 2.4 Install go2_rl_gym
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Enter the directory and install:
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```bash
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cd go2_rl_gym
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pip install -e .
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```
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### 2.5 Real Robot Deployment (Optional)
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#### 2.5.1 unitree_sdk2
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C++ SDK. For compilation, please refer to the [official tutorial](https://github.com/unitreerobotics/unitree_sdk2?tab=readme-ov-file#environment-setup).
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#### 2.5.2 unitree_sdk2_python (Choose for Python Deployment)
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```bash
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conda create -n kaiwu python=3.8
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conda activate kaiwu
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pip3 install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
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git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
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cd unitree_sdk2_python
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pip install -e .
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```
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#### 2.5.3 Install unitree_cpp_deploy (Choose for C++ Deployment)
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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](https://github.com/wty-yy/unitree_cpp_deploy).
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### 2.6 RoboGauge Evaluation (Optional)
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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](https://github.com/wty-yy/RoboGauge).
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```bash
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git clone [https://github.com/wty-yy/RoboGauge.git](https://github.com/wty-yy/RoboGauge.git)
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cd RoboGauge
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pip install -e .
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```
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