This repository curates papers, resources, and industry trends in Embodied AI, a rapidly advancing field focused on intelligent agents interacting with physical or simulated environments. It serves researchers and practitioners seeking to stay abreast of breakthroughs, particularly those involving Large Language Models (LLMs) in robotics and agent development.

How It Works

The project functions as a living bibliography, systematically collecting and categorizing seminal and recent works. It highlights key areas like LLM-driven robotics, navigation, and vision-language tasks, providing links to papers, surveys, and related projects. This structured approach allows users to quickly identify relevant research and understand the evolving landscape of embodied intelligence.

Quick Start & Requirements

No installation or specific requirements are needed to browse the curated list. Links to external resources may have their own prerequisites.

Highlighted Details

• Extensive collection of survey papers on Embodied AI and LLM-based agents.
• Categorization of research by sub-fields such as Navigation, R&D Trendings, and Vision-Language Tasks.
• Inclusion of industry-driven resources, workshops, and tutorials.
• Features prominent LLM-driven agent frameworks like AutoGen, MetaGPT, and Voyager.

Maintenance & Community

The repository is actively maintained, with contributions welcomed via pull requests. It encourages community engagement through stars and sharing. Links to related repositories and specific researchers are provided, fostering a collaborative environment.

Licensing & Compatibility

The repository itself is not software but a curated list. The licensing of linked papers and projects varies and must be checked individually.

Limitations & Caveats

As a curated list, the content's depth and breadth are dependent on the maintainer's and community's contributions. It does not provide code or direct implementations, requiring users to seek out and evaluate individual projects.