Embodied-AI-Guide/README.md

<h1 align="center">Embodied-AI-Guide-zh (In Progress) [<a href="https://github.com/tianxingchen/Embodied-AI-Guide">Github Repo</a>]</h1>

<p align="center"><b>🦉Contributors</b>: <a href="https://tianxingchen.github.io">Tianxing Chen (陈天行)</a>, <a href="https://github.com/yudezou">Yude Zou (邹誉德)</a>, <a href="https://github.com/zanxinchen">Zanxin Chen (陈攒鑫)</a>, <a href="https://github.com/27yw">Wen Ye (叶雯)</a></p>
<p align="center"><i>Latest Update: Sept 1, 2024</i></p>


> Embodied AI (具身智能)入门的路径以及useful信息的总结，期望是按照路线走完后，新手可以快速建立关于这个领域的认知，希望能帮助到各位入门具身智能的朋友,欢迎star与PR🌟~<br>文章中引用文章的原作者，我们在[🙏 Acknowledgement - 致谢](#acknowledgement)部分进行了致谢，感谢他们的分享🌹<br><a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Ftianxingchen%2FEmbodied-AI-Guide&count_bg=%232B8DD9&title_bg=%237834C6&icon=github.svg&icon_color=%23E7E7E7&title=Page+Viewers&edge_flat=false"/></a>

# Contents - 目录
<nav>
  <ul>
    <li><a href="#start">Start Up - 从这里开始</a></li>
    <li><a href="#rl">Reinforcement Learning - 强化学习</a></li>
    <li><a href="#il">Imitation Learning - 模仿学习</a></li>
    <li><a href="#llm_robot">Large Language Model for Robotics - 大模型在机器人学中的应用</a></li>
    <li><a href="#3dv">3D Vision - 三维视觉</a></li>
    <li><a href="#benchmarks">Benchmarks - 基准</a></li>
    <li><a href="#info">Useful Info - 有利于搭建认知的资料</a></li>
    <li><a href="#communities">Communities - 社区</a></li>
    <li><a href="#companies">Companies - 公司</a></li>
    <li><a href="#acknowledgement">🙏 Acknowledgement - 致谢</a></li>
  </ul>
</nav>

<section id="start"></section>

# Start Up - 从这里开始

**什么是具身智能？**<br>
具身智能是指一种基于物理身体进行感知和行动的智能系统，其通过智能体与环境的交互获取信息、理解问题、做出决策并实现行动，从而产生智能行为和适应性。

<section id="rl"></section>

# Reinforcement Learning - 强化学习
台湾大学李宏毅公开课: [bilibili](https://www.bilibili.com/video/BV1XP4y1d7Bk/?spm_id_from=333.337.search-card.all.click&vd_source=ab9cf5374617c2867aaea34af29b53c9)<br>
EasyRL - 蘑菇书: [website](https://datawhalechina.github.io/easy-rl/#/)<br>
强化学习的数学原理 - 西湖大学赵世钰: [bilibili](https://space.bilibili.com/2044042934/channel/collectiondetail?sid=748665)<br>
实践[gymnasium](https://gymnasium.farama.org/)，可以尝试一下把玩一下登月着陆等经典强化学习场景，思考+动手，观察阶段agent的表现并分析，有助于深入理解强化学习

<section id="il"></section>

# Imitation Learning - 模仿学习
模仿学习简洁教程 - 南京大学LAMDA: [PDF](https://www.lamda.nju.edu.cn/xut/Imitation_Learning.pdf)
Supervised Policy Learning for Real Robots, RSS 2024 Workshop 教程：真实机器人的监督策略学习, [bilibili](https://www.bilibili.com/video/BV1Fx4y1s7if/?buvid=XY415384A771A6C681C9BEB3817566ED57724&is_story_h5=false&mid=ORgXkVzTHaOKTsml0RX5Gw%3D%3D&plat_id=240&share_from=ugc&share_medium=android&share_plat=android&share_source=WEIXIN&share_tag=s_i&spmid=dt.space-dt.0.0&timestamp=1721464513&unique_k=Cqj5d9J&up_id=2185804&vd_source=ab9cf5374617c2867aaea34af29b53c9)

<section id="llm_robot"></section>

# Large Language Model for Robotics - 大模型在机器人学中的应用
Robotics+LLM系列通过大语言模型控制机器人 [2]: [zhihu](https://zhuanlan.zhihu.com/p/668053911)<br>
PDDL-wiki: [website](https://planning.wiki/)<br>
An Introduction to PDDL: [PDF](https://www.cs.toronto.edu/~sheila/2542/s14/A1/introtopddl2.pdf)<br>
AI Agent from IBM: An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.<br>
Embodied Agent wiki: [website](https://en.wikipedia.org/wiki/Embodied_agent)<br>
Awesome-LLM-Robotics: A repo contains a curative list of papers using Large Language/Multi-Modal Models for Robotics/RL. [website](https://github.com/GT-RIPL/Awesome-LLM-Robotics)<br>
Lilian Weng 个人博客 - AI Agent 系统综述 [5]: 中文: [website](https://mp.weixin.qq.com/s/Jb8HBbaKYXXxTSQOBsP5Wg) 英文: [website](https://lilianweng.github.io/posts/2023-06-23-agent/)<br>


<section id="3dv"></section>

# 3D Vision - 三维视觉
三维视觉导论 - Andreas Geiger: [website](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/)

## 3DGS
3D Gaussian Splatting原理速通: [bilibili](https://www.bilibili.com/video/BV11e411n79b/?spm_id_from=333.788&vd_source=ab9cf5374617c2867aaea34af29b53c9)


<section id="benchmarks"></section>

# Benchmarks & Simulators - 基准 & 仿真器
具身智能常用benchmark总结 [1]: [zhihu](https://zhuanlan.zhihu.com/p/695342864)

常见仿真器wiki: [wiki](https://simulately.wiki/)


<section id="info"></section>

# Useful Info - 有利于搭建认知的资料
具身智能基础技术路线-YunlongDong [2]: [PDF](./files/具身智能基础技术路线-YunlongDong.pdf), [bilibili](https://www.bilibili.com/video/BV1d5ukedEsi/?buvid=XXCD799C01878A6CFDECF3FB4427E2F070877&from_spmid=default-value&is_story_h5=false&mid=iWFclAyh36UYMh2G6ZcsDw%3D%3D&p=1&plat_id=114&share_from=ugc&share_medium=android&share_plat=android&share_session_id=9c0dccf5-ec0b-4369-8b89-ff1d848467ee&share_source=WEIXIN&share_tag=s_i&spmid=united.player-video-detail.0.0&timestamp=1716466406&unique_k=Q0CaIUj&up_id=249218043)

AI领域值得关注的博主列表 [3]: [zhihu](https://zhuanlan.zhihu.com/p/682110383)

Robotics实验室总结 [4]: [zhihu_1](https://zhuanlan.zhihu.com/p/682671294?utm_psn=1782122763157188608), [zhihu_2](https://zhuanlan.zhihu.com/p/682692024?utm_psn=1782122945184796672)


<section id="communities"></section>

# Communities - 社区
DeepTimber Robotics Innovations Community, 深木科研交流社区: [website](https://gamma.app/public/DeepTimber-Robotics-Innovations-Community-A-Community-for-Multi-m-og0uv8mswl1a3q7?mode=doc)

<section id="acknowledgement"></section>

<a name="acknowledgement"></a>

<section id="companies"></section>

# Companies - 公司
松灵AgileX: [website](https://www.agilex.ai/)

# 🙏 Acknowledgement - 致谢
本文转载/引用了大量博主的文章，我们对他们的知识分享表示感谢，引用列表如下：
| Since 2024 🌹 |  |  |  |
|------|------|------|------|
| [1] 知乎[穆尧](https://www.zhihu.com/people/mu-yao-12-34) | [2] 知乎[东林钟声](https://www.zhihu.com/people/dong-lin-zhong-sheng-76), Github[Yunlong Dong](https://github.com/yunlongdong) | [3] 知乎[强化学徒](https://www.zhihu.com/people/heda-he-28) | [4] 知乎[Biang哥](https://www.zhihu.com/people/qi-da-guang) | [5] OpenAI [Lilian Weng](https://lilianweng.github.io/) |

# 🏷️ License - 许可证
This repository is released under the MIT license. See LICENSE for additional details.