mirror of https://github.com/fan-ziqi/rl_sar.git
106 lines
2.3 KiB
Markdown
106 lines
2.3 KiB
Markdown
# rl_sar
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[English document](README.md)
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四足机器人强化学习算法的仿真验证与实物部署。"sar"代表"simulation and real"
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## 准备
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拉取代码（同步拉取子模块）
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```bash
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git clone --recursive https://github.com/fan-ziqi/rl_sar.git
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```
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如有更新：
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```bash
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git pull
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git submodule update --remote --recursive
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```
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在任意位置下载并部署`libtorch`
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```bash
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cd /path/to/your/torchlib
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wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.0.1%2Bcpu.zip
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unzip libtorch-cxx11-abi-shared-with-deps-2.0.1+cpu.zip -d ./
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echo 'export Torch_DIR=/path/to/your/torchlib' >> ~/.bashrc
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```
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安装 `teleop-twist-keyboard`
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```bash
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sudo apt install ros-noetic-teleop-twist-keyboard
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```
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## 编译
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自定义代码中的以下两个函数，以适配不同的模型：
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```cpp
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torch::Tensor forward() override;
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torch::Tensor compute_observation() override;
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```
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然后到根目录编译
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```bash
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cd ..
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catkin build
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```
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## 运行
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运行前请将训练好的pt模型文件拷贝到`rl_sar/src/rl_sar/models`中
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### 仿真
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新建终端，启动gazebo仿真环境
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```bash
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source devel/setup.bash
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roslaunch rl_sar start_env.launch
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```
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新建终端，启动控制程序
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```bash
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source devel/setup.bash
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rosrun rl_sar rl_sim
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```
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新建终端，键盘控制程序
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```bash
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rosrun teleop_twist_keyboard teleop_twist_keyboard.py
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```
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### 实物
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与实物的连接可分为无线与有线形式
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* 无线：连接机器人发出的Unitree开头的WIFI **（注意：无线连接可能会出现丢包断联甚至失控，请注意安全）**
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* 有线：用网线连接计算机和机器人的任意网口，配置计算机ip为192.168.123.162，网关255.255.255.0
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新建终端，启动控制程序
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```bash
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source devel/setup.bash
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rosrun rl_sar rl_real
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```
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按下遥控器的**R2**键让机器人切换到默认站起姿态，按下**R1**键切换到RL控制模式，任意状态按下**L2**切换到最初的趴下姿态。左摇杆上下控制x左右控制yaw，右摇杆左右控制y。
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## 引用
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如果您使用此代码或其部分内容，请引用以下内容：
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```
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@software{fan-ziqi2024rl_sar,
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author = {fan-ziqi},
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title = {{rl_sar: Simulation Verification and Physical Deployment of the Quadruped Robot's Reinforcement Learning Algorithm.}},
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url = {https://github.com/fan-ziqi/rl_sar},
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year = {2024}
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}
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``` |