navrl_environmrnt_setting

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navrl_environmrnt_setting

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# navrl_environmrnt_setting
参考：
[https://github.com/Zhefan-Xu/NavRL?tab=readme-ov-file](https://github.com/Zhefan-Xu/NavRL?tab=readme-ov-file)
[https://github.com/Zhefan-Xu/isaac-go2-ros2](https://github.com/Zhefan-Xu/isaac-go2-ros2)
## 1. Isaac Sim 安装

博主这边是按照github上的建议，拉的docker镜像，这样有利于隔离本机原本的isaac sim环境。
1）download Isaac Sim version 2023.1.0-hotfix.1：
	
	```
	# 容器下载，如果已经有容器，则可以跳过
	# Docker installation using  the convenience script
	curl -fsSL https://get.docker.com -o get-docker.sh
	sudo sh get-docker.sh
	# Post-install steps for Docker
	sudo groupadd docker
	sudo usermod -aG docker $USER
	newgrp docker
	# Verify Docker
	docker run hello-world
	```
2）Then, download the Isaac Sim to your docker container:
	
```
	#拉取指定镜像
	docker pull nvcr.io/nvidia/isaac-sim:2023.1.0-hotfix.1
    #运行 isaac-sim
    docker run --name isaac-sim --entrypoint bash -it --runtime=nvidia --gpus all -e "ACCEPT_EULA=Y" --rm --network=host \
    -e "PRIVACY_CONSENT=Y" \
    -v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache:rw \
    -v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \
    -v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \
    -v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \
    -v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \
    -v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \
    -v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \
    -v ~/docker/isaac-sim/documents:/root/Documents:rw \
    nvcr.io/nvidia/isaac-sim:2023.1.0-hotfix.1
```

3）Move the downloaded Isaac Sim from the docker container to your local machine:
	
	bash docker ps # check your container ID in another terminal
	# Replace <id_container> with the output from the previous command
	docker cp <id_container>:isaac-sim/. /path/to/local/folder # absolute path

**Isaac Sim version 2023.1.0-hotfix.1 is now installed on your local machine.**

## 2. NavRL 训练环境设置
1）设置环境变量，创建虚拟环境

```
# Set the ISAACSIM_PATH environment variable
echo 'export ISAACSIM_PATH="path/to/isaac_sim-2023.1.0-hotfix.1"' >> ~/.bashrc

git clone https://github.com/Zhefan-Xu/NavRL.git
cd ~/NavRL/isaac-training
bash setup.sh  # 这里会创建一个名为NavRL的虚拟环境
```
2）测试安装正确并运行一个训练demo

```
# Activate NavRL virtual environment
conda activate NavRL
# Run a training example with default settings
python training/scripts/train.py
```
使用默认参数运行一个包含两个机器人的训练示例来验证安装。
![请添加图片描述](https://i-blog.csdnimg.cn/direct/2c8b7fa0c2ff459f976d5e6d614793fb.png)

## 3. 训练你自己的RL策略
训练环境设置和卫生参数可在NavRL/isaac-training/train/cfg中找到。
以下示例展示了1024个机器人、350个静态障碍物和80个动态障碍物(需要使用RTX 4090)进行训练。我们建议使用 Wandb 来监控您的培训和评估统计数据。需要注意的是，这里运行会离线渲染，不会打开isaac sim。训练示意 [viedo](https://private-user-images.githubusercontent.com/55560905/415915791-2294bd94-69b3-4ce8-8e91-0118cfae9bcd.mp4?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.49-aX4aRVrszLvBAAp2OIcd8w4NfVZCO7ByE0jVLuR4).
```
python training/scripts/train.py headless=True env.num_envs=1024 env.num_obstacles=350 \
env_dyn.num_obstacles=80 wandb.mode=online
```

## 4. NavRL ROS2 部署--环境搭建
博主这里只验证了ROS2环境，ROS1 可以参考[NavRL](https://github.com/Zhefan-Xu/NavRL?tab=readme-ov-file)
本节演示了使用 Unitree Go2 四足机器人使用 ROS2 和 Isaac Sim 部署 NavRL 的示例。确保您的系统满足以下要求:

```
Ubuntu 22.04 LTS
ROS2 Humble
```

开始之前,请根据此[链接](https://github.com/Zhefan-Xu/isaac-go2-ros2)安装模拟器。这个验证环境也是原作者写的一个仓库，博主就是从这里开始运到各种问题！！！
1. 作者这个验证环境是基于 Isaac Sim 4.2.0 and Isaac Lab 1.2.0 版本的，必须严格对应。同时，博主不建议用pip来安装isaacsim，这回产生各种包不兼容的问题，直接以通过源码安装即可。
	```
	# ！！！！ pip 安装示意，十分不建议！！
	pip install 'isaacsim[all,extscache]==4.2.0.1' --extra-index-url https://pypi.nvidia.com
	```
2. 源码安装 Isaac Sim 4.2.0 and Isaac Lab 1.2.0 
1） 从官网下载 IsaacSim 4.2.0 安装包
[https://download.isaacsim.omniverse.nvidia.com/isaac-sim-standalone%404.2.0-rc.18%2Brelease.16044.3b2ed111.gl.linux-x86_64.release.zip](https://download.isaacsim.omniverse.nvidia.com/isaac-sim-standalone@4.2.0-rc.18+release.16044.3b2ed111.gl.linux-x86_64.release.zip)
2）安装IsaacSim 4.2.0
	```
	mkdir ~/isaacsim
	cd ~/Downloads
	unzip isaac-sim-standalone@4.2.0-rc.18+release.16044.3b2ed111.gl.linux-x86_64.release.zip -d ~/isaacsim
	cd ~/isaacsim
	./omni.isaac.sim.post.install.sh
	./isaac-sim.selector.sh
	```
	其中，执行 ./omni.isaac.sim.post.install.sh 需要等待一段时间，完成后终端输出信息为：
	![在这里插入图片描述](https://i-blog.csdnimg.cn/direct/50352248b9804c27ba74b0f372bb579f.png)
	最后 ./isaac-sim.selector.sh 弹出如下窗口：
![在这里插入图片描述](https://i-blog.csdnimg.cn/direct/417c9cdace4e45019dba0b28a98c2cd3.png)
点击 START ，即可弹出如下窗口：
![在这里插入图片描述](https://i-blog.csdnimg.cn/direct/fea22780956042fb8c6605d2814a0186.png)
2）安装Isaac Lab 1.2.0
安装文档：
[https://isaac-sim.github.io/IsaacLab/v1.2.0/index.html](https://isaac-sim.github.io/IsaacLab/v1.2.0/index.html)
github 直接安装：
[https://github.com/isaac-sim/IsaacLab/releases/tag/v1.2.0](https://github.com/isaac-sim/IsaacLab/releases/tag/v1.2.0)
3）创建Isaac Sim符号链接
**注意！！！一定要先创建符号连接，再配置环境，否则会找不到isaacsim包**
	```
	cd IsaacLab
	# 创建软链接到你的isaac sim目录下
	# 该命令里的 path_to_isaac_sim 要替换成你isaac-sim所在的目录，如我的是：
	# /home/ai/omniverse/pkg/isaac-sim-4.1.0 
	
	ln -s path_to_isaac_sim _isaac_sim
	
	# 我的软链接命令
	# ln -s /home/ai/omniverse/pkg/isaac-sim-4.1.0 _isaac_sim
	```
	4）配置环境
	
	```
	#创建虚拟环境
	./isaaclab.sh --conda # 创建conda环境，默认名字为 isaaclab 
	 conda activate isaaclab
	#安装依赖
	sudo apt install cmake build-essential 
	cd IsaacLab 
	./isaaclab.sh --install
	```
	5）验证安装 
	有两种开启方法，由于我们上面使用conda部署了环境，所以建议在conda虚拟环境里直接使用python命令来运行例子，即这里的方法2：
	
	```
	# 方法1: Using the isaaclab.sh executable
	./isaaclab.sh -p source/standalone/tutorials/00_sim/launch_app.py
	
	# 方法2: Using python in your virtual environment
	python source/standalone/tutorials/00_sim/launch_app.py
	```
	如果看到了这个，那么安装就成功了！![在这里插入图片描述](https://i-blog.csdnimg.cn/direct/32ea756ec9b6468a81985ffd433ffa8e.png)

3. 配置[Isaac Sim Unitree Go2 ROS2](https://github.com/Zhefan-Xu/isaac-go2-ros2)--作者写的验证框架
安装相关依赖
	```
	conda activate isaaclab      # 这里的isaaclab就是前面 **4）配置环境**步骤生成的虚拟环境
	#这一步比较重要，这里将虚拟环境与ros2桥接在一起
	conda install -c conda-forge libstdcxx-ng 
	```
	拉仓库运行测试
	```
	git clone https://github.com/Zhefan-Xu/isaac-go2-ros2.git
	conda activate isaaclab
    python isaac_go2_ros2.py
	```
	模拟加载完成后,机器人可以由键盘进行远程操作:
	
	```
	W 向前，A左转，S:向后，D:右，Z:左转，C:右转。
	```

4. 遇到的问题
	博主在`python isaac_go2_ros2.py`这一步遇到很多问题，具体原因我怀疑是torch版本不匹配，但是实际上指定版本后，torch版本应该是和Isaac Sim 4.2.0 and Isaac Lab 1.2.0绑定的，为torch2.4.0，按道理应该和作者环境一致了。
	
	遇到的问题主要是在初始化实例的时候，某些参数对应不上，或者包None的错误，导致其他参数位置对应不上：
	1. `ValueError: Included color maps are: dict_keys(['magma', 'inferno', 'plasma', 'viridis']). If you want to use a matplotlib color map you can pass it as color_map=matplotlib.pyplot.get_cmap(name) Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. Fatal Python error: Segmentation fault`
	原因：Isaac lab 传给 color_meshes_by_height 的 color_map 不是内置的 ['magma','inferno','plasma','viridis']，所以抛异常。
	解决方案：打开 ~/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_generator.py
		```
		if self.cfg.color_scheme == "height":
	      self.terrain_mesh = color_meshes_by_height(self.terrain_mesh)
		改成
	    if self.cfg.color_scheme == "height":
          self.terrain_mesh = color_meshes_by_height([self.terrain_mesh], color_map="viridis")
		```
	2.	`TypeError: xxxx   _MISSING_TYPE'错误`
	原因，作者在初始化实例的时候，某些参数默认没有传递，导致出错，主要发生在：
	1 ）~/isaac-go2-ros2/go2/go2_env.py
	具体报错：
		```
		File "~/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/envs/mdp/commands/velocity_command.py", line 136, in _update_command
		    self.cfg.heading_control_stiffness * heading_error,
		TypeError: unsupported operand type(s) for *: '_MISSING_TYPE' and 'Tensor'
		```
		这里只是其中一个type报错，实际上还有很多，都集中在下面的解决方案里了
		解决方案：
		```
		#将 ~/isaac-go2-ros2/go2/go2_env.py代码中
		
		class CommandsCfg:
		"""Command specifications for the MDP."""
		base_vel_cmd = mdp.UniformVelocityCommandCfg(
		    asset_name="unitree_go2",
		    resampling_time_range=(0.0, 0.0),
		    debug_vis=True,
		    ranges=mdp.UniformVelocityCommandCfg.Ranges(
		        lin_vel_x=(0.0, 0.0), lin_vel_y=(0.0, 0.0), ang_vel_z=(0.0, 0.0), heading=(0, 0)
		    ),
		)
		**改成**
		class CommandsCfg:
		"""Command specifications for the MDP."""
		base_vel_cmd = mdp.UniformVelocityCommandCfg(
		    asset_name="unitree_go2",
		    resampling_time_range=(1.0, 5.0), 
		    rel_heading_envs=1.0,
		    rel_standing_envs=0.0,  
		    debug_vis=True,
		    heading_control_stiffness=0.0,
		    ranges=mdp.UniformVelocityCommandCfg.Ranges(
		        lin_vel_x=(0.0, 0.0), lin_vel_y=(0.0, 0.0), ang_vel_z=(0.0, 0.0), heading=(0, 0)
		    ),
		)
		```
		
		2）~/isaac-go2-ros2/env/sim_env.py
			具体报错：
		```
		~/IsaacLab/source/extensions/omni.isaac.lab/omni/isaac/lab/terrains/terrain_importer.py
		中这一行
		############################
		 self.terrain_levels = torch.randint(
		    0, max_init_level + 1, (num_envs,), device=dev
		)
		############################
		TypeError: randint() received an invalid combination of arguments - got (int, int, tuple, device=str), but expected one of:
		 * (int high, tuple of ints size, *, torch.Generator generator, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
		 * (int high, tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
		 * (int low, int high, tuple of ints size, *, torch.Generator generator, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
		 * (int low, int high, tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
		```
		实际上这里代码是没有错的，而是在实例化的时候，作者少传了**num_envs**这个参数
		```
		#在 ~/isaac-go2-ros2/env/sim_env.py 每一个出现的地方
		prim_path="/World/obstacleTerrain", 
		#下面补充一行
		num_envs=1,
		```
## 5. NavRL ROS2 部署--部署运行训练好的policy

首先,将 NavRL/ros2 文件夹从该仓库复制到您的 ros2 工作区中。
```
cp -r ros2 /path/to/ros2_ws/src 
colcon build --symlink-install
```
然后,启动模拟并部署NavRL导航。

```
# Launch Isaac Go2 simulator
conda activate isaaclab
cd /path/to/isaac-go2-ros2
python isaac-go2-ros2.py

# Start the perception and safety module
ros2 launch navigation_runner perception.launch.py
ros2 launch navigation_runner safe_action.launch.py # optional

# Turn on Rviz2 visualization
ros2 launch navigation_runner rviz.launch.py

# Run the navigation launch
conda activate NavRL
conda install -c conda-forge libstdcxx-ng  # 这里也要下载这个依赖，作者把这个忽略了
# 这里可能会出现一些缺少依赖包的问题，直接pip安装即可
ros2 launch navigation_runner navigation.launch.py
```

Isaac Sim 窗口将显示环境,而 RViz 窗口则显示数据。使用 RViz 的 2D 导航目标工具来设定导航目标。实验结果见：[https://www.bilibili.com/video/BV1WjJCzFExD/?vd_source=0605977334fda3406a6c82619c3fb0d3](https://www.bilibili.com/video/BV1WjJCzFExD/?vd_source=0605977334fda3406a6c82619c3fb0d3)

navrl_environmrnt_setting

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