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v1.1.1-rc2; add README

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2026-01-27 21:36:44 +08:00
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# Installation Guide
# Installation and Configuration Guide
## System Requirements
- **Operating System**: Recommended Ubuntu 18.04 or later
- **GPU**: Nvidia GPU
- **Driver Version**: Recommended version 525 or later
- **OS**: Ubuntu 18.04 or higher is recommended
- **GPU**: Nvidia GPU
- **Driver Version**: Version 525 or higher is recommended
---
## 1. Creating a Virtual Environment
## 1. Create 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.
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:
MiniConda is a lightweight distribution of Conda suitable for creating and managing virtual environments. Use the following commands to download and install:
```bash
mkdir -p ~/miniconda3
@@ -30,7 +30,7 @@ After installation, initialize Conda:
source ~/.bashrc
```
### 1.2 Create a New Environment
### 1.2 Create New Environment
Use the following command to create a virtual environment:
@@ -38,7 +38,7 @@ Use the following command to create a virtual environment:
conda create -n unitree-rl python=3.8
```
### 1.3 Activate the Virtual Environment
### 1.3 Activate Virtual Environment
```bash
conda activate unitree-rl
@@ -46,7 +46,7 @@ conda activate unitree-rl
---
## 2. Installing Dependencies
## 2. Install Dependencies
### 2.1 Install PyTorch
@@ -58,15 +58,15 @@ conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=
### 2.2 Install Isaac Gym
Isaac Gym is a rigid body simulation and training framework provided by Nvidia.
Isaac Gym is Nvidia's rigid body simulation and training framework.
#### 2.2.1 Download
Download [Isaac Gym](https://developer.nvidia.com/isaac-gym) from Nvidia’s official website.
Download [Isaac Gym](https://developer.nvidia.com/isaac-gym) from the Nvidia official website.
#### 2.2.2 Install
After extracting the package, navigate to the `isaacgym/python` folder and install it using the following commands:
Unzip the file, enter the `isaacgym/python` folder, and execute the following command to install:
```bash
cd isaacgym/python
@@ -75,18 +75,24 @@ pip install -e .
#### 2.2.3 Verify Installation
Run the following command. If a window opens displaying 1080 balls falling, the installation was successful:
Run the following commands. If a window pops up showing 1080 balls falling, the installation is successful:
```bash
cd examples
python 1080_balls_of_solitude.py
```
If you encounter any issues, refer to the official documentation at `isaacgym/docs/index.html`.
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 library implementing reinforcement learning algorithms.
`rsl_rl` is a reinforcement learning algorithm library.
Our repository includes `rsl_rl` with new algorithms. Clone the Git repository:
```bash
git clone https://github.com/wty-yy/go2_rl_gym.git
```
#### 2.3.1 Install
@@ -97,23 +103,41 @@ pip install -e .
### 2.4 Install go2_rl_gym
#### 2.4.1 Download
Clone the repository using Git:
```bash
git clone https://github.com/unitreerobotics/go2_rl_gym.git
```
#### 2.4.2 Install
Navigate to the directory and install it:
Enter the directory and install:
```bash
cd go2_rl_gym
pip install -e .
```
### 2.5 Install unitree_cpp_deploy (Optional)
### 2.5 Real Robot Deployment (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](https://github.com/wty-yy-mini/unitree_cpp_deploy).
#### 2.5.1 unitree_sdk2
C++ SDK. For compilation, please refer to the [official tutorial](https://github.com/unitreerobotics/unitree_sdk2?tab=readme-ov-file#environment-setup).
#### 2.5.2 unitree_sdk2_python (Choose for Python Deployment)
```bash
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](https://github.com/wty-yy-mini/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](https://github.com/wty-yy/RoboGauge).
```bash
git clone [https://github.com/wty-yy/RoboGauge.git](https://github.com/wty-yy/RoboGauge.git)
cd RoboGauge
pip install -e .
```