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tianxingchen/embodied-ai-guide

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TLDR

A Chinese-language encyclopedia and hands-on guide for embodied AI, robots that perceive and act in the physical world, covering tutorials, algorithms, hardware platforms, simulation environments, and community resources for newcomers.

Mindmap

mindmap
  root((embodied-ai-guide))
    What It Is
      Chinese knowledge base
      Robotics AI encyclopedia
      Community maintained
    Learning Path
      One-week tutorial
      RoboTwin 2.0 platform
      GPU required
    Algorithm Areas
      Robot learning methods
      Visual foundation models
      Vision-language-action
    Resources
      Hardware platforms
      Simulation environments
      Research datasets
    Community
      Chinese public accounts
      Research blogs
      Conference links
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Code map

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Things people build with this

USE CASE 1

Follow a one-week tutorial to collect robot movement data, train a policy, and evaluate performance using the RoboTwin 2.0 simulation platform.

USE CASE 2

Use the algorithm index to quickly survey robot learning methods, visual foundation models, and vision-language-action research before diving deeper.

USE CASE 3

Find hardware platforms, simulation environments, and real-robot datasets used by embodied AI researchers to develop and test systems.

USE CASE 4

Discover Chinese research blogs, paper reading lists, and top academic conferences in the embodied AI field.

Tech stack

RoboTwin 2.0

Getting it running

Difficulty · hard Time to first run · 1day+

The hands-on tutorial requires a GPU with at least 16GB of video memory to run the RoboTwin 2.0 simulation.

No license information is provided in the explanation.

In plain English

Embodied-AI-Guide is a Chinese-language knowledge base and resource index for the field of embodied AI, which refers to AI systems that perceive their surroundings and act through a physical body, most commonly a robot. The project is maintained by the Lumina Embodied AI community and is structured like an encyclopedia, covering the major techniques, tools, papers, and learning paths in the field. The guide is organized around several main areas. One section provides a hands-on tutorial designed to be completed in roughly a week, walking through a full workflow using the RoboTwin 2.0 simulation platform: collecting robot movement data, training a policy (the decision-making model), and evaluating how well the robot performs a task. This section targets beginners and requires a GPU with at least 16GB of video memory to run. Another large section covers the algorithm landscape, organized from foundational building blocks up through higher-level decision systems. Topics include robot learning methods, visual foundation models, language models applied to robotics, and vision-language-action models that combine perception with physical action. Computer vision and graphics techniques relevant to robot sensing are also covered. The guide also indexes hardware platforms, simulation environments, real-robot datasets, and benchmark collections that researchers use to develop and test embodied AI systems. Community resources are included: Chinese public accounts, research blogs, paper reading lists, and links to top academic conferences and journals in the area. The project is aimed at Chinese-speaking newcomers who want to quickly build a mental map of embodied AI before diving into specific algorithms or engineering. The README is primarily in Chinese, though section titles and linked resources often include English.

Copy-paste prompts

Prompt 1
Give me a beginner study plan for learning embodied AI from scratch using the topics covered in the Embodied-AI-Guide, starting with the RoboTwin 2.0 tutorial.
Prompt 2
Based on the Embodied-AI-Guide, explain the difference between robot learning methods and vision-language-action models in plain English for someone with no robotics background.
Prompt 3
I want to run the RoboTwin 2.0 tutorial from Embodied-AI-Guide. Walk me through the steps: collecting movement data, training a policy, and evaluating the result.
Prompt 4
What simulation environments and real-robot datasets does the Embodied-AI-Guide recommend for beginners who want hands-on practice without real hardware?
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