This guide provides a structured introduction to Embodied AI research, targeting beginners and students. It aims to clarify the field's scope, address common misconceptions, and establish sound research principles, serving as an official onboarding resource for PKU EPIC Lab undergraduates. The benefit lies in offering a clear roadmap to navigate the rapidly growing, yet sometimes quality-variable, domain of Embodied AI.

How It Works

The guide emphasizes understanding core concepts: Embodied AI agents interact with environments via perception and action, differing from traditional AI's static data focus. A key challenge highlighted is the high cost and limited scale of robot interaction data. The approach prioritizes clear task definition and justification for using learning methods over traditional robotics techniques, encouraging critical thinking about research problems and settings before diving into specific models.

Quick Start & Requirements

This guide requires foundational knowledge in Python, Conda, PyTorch, Linux Shell, Git, SSH, LLMs, Cursor, and Docker. Recommended learning resources include introductory courses on Embodied AI (or similar robotics foundations), Computer Vision (e.g., Stanford CS231N), and optionally Deep Reinforcement Learning (e.g., Berkeley CS285). Familiarity with simulation environments like IsaacLab or MuJoCo is also advised for practical application.

Highlighted Details

• Addresses the critical data bottleneck in Embodied AI, discussing strategies like scaling data via simulation (e.g., GraspVLA) and leveraging existing data (e.g., Diffusion Policy).
• Stresses the importance of task formulation and justifying the necessity of learning-based approaches over traditional control and planning methods.
• Provides a comprehensive overview of key research areas including Grasping, Manipulation, Navigation, and Locomotion, detailing common tasks and methodologies.
• Covers modern learning paradigms such as Few-shot Imitation Learning, Robot Foundation Models (VLA), Sim-to-Real Reinforcement Learning, and World Models.

Maintenance & Community

The guide is developed and maintained by the PKU EPIC Lab. While specific community channels like Discord or Slack are not mentioned, the project is associated with the GitHub repository jiangranlv/embodied-ai-start (and potentially TianxingChen/Embodied-AI-Guide as a related resource).

Licensing & Compatibility

The project is explicitly marked with "All rights reserved. Commercial distribution prohibited." This restrictive license significantly limits its applicability for commercial use or integration into proprietary systems.

Limitations & Caveats

The primary limitation is the non-commercial, all-rights-reserved license, prohibiting commercial distribution. The guide itself is an introductory resource and does not provide executable code or research tools, but rather a conceptual framework and learning path for the field of Embodied AI.