今天下午主要是在实验室熟悉隧道烟雾的实验数据收集
以及视频转图像还有图像差分的一些python文件

lehome规则已发布https://lehome-challenge.com/simulation-challenge
按照对应github链接配置下环境

==uv uv.lock pyproject.toml==能代替大部分conda pip与requirements的任务了，并且在某些地方要做的更加好。

基本环境

1. Clone the Repository

git clone https://github.com/lehome-official/lehome-challenge.git
cd lehome-challenge

2. Install Dependencies with uv

uv sync

This will create a virtual environment and install all required dependencies.
这一步好久好久 要留有足够的硬盘空间

3. Clone and Configure IsaacLab

cd third_party
git clone https://github.com/lehome-official/IsaacLab.git
cd ..

第三方的一般都在third_party，意为第三方插件

4. Install IsaacLab

Activate the virtual environment and install IsaacLab:

source .venv/bin/activate
./third_party/IsaacLab/isaaclab.sh -i none

5. Install LeHome Package

Finally, install the LeHome package in development mode:

uv pip install -e ./source/lehome

资源和数据集

Download the required simulation assets (scenes, objects, robots) from HuggingFace:

# This creates the Assets/ directory with all required simulation resources
hf download lehome/asset_challenge --repo-type dataset --local-dir Assets

Download Example Dataset  

We provide demonstrations for four types of garments. Download from HuggingFace:

hf download lehome/dataset_challenge_merged --repo-type dataset --local-dir Datasets/example

If you need depth information or individual data for each garment. Download from HuggingFace:

hf download lehome/dataset_challenge --repo-type dataset --local-dir Datasets/example

到数据采集这里 前面两章讲的是怎么使用主控臂采集 后面讲的是怎么使用键盘采集 给了一些cli命令还有对应的参数 但是我感觉键盘采集不是很现实

后面再看一下ebcloud要怎么部署吧 然后端口转发什么什么的