How can LLMs improve embodied multi-agent collaboration?
Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review
February 18, 2025
https://arxiv.org/pdf/2502.11518This paper surveys the emerging field of generative multi-agent collaboration in embodied AI, focusing on how large foundation models (FMs), particularly Large Language Models (LLMs), are transforming multi-agent systems interacting with the physical or simulated world.
Key points for LLM-based multi-agent systems:
- Generative agents enhance collaboration: LLMs enable richer communication, adaptive planning, and flexible problem-solving within multi-agent systems.
- Extrinsic vs. intrinsic collaboration: Multi-agent collaboration can occur between multiple physically (or virtually) embodied agents (extrinsic) or between different functional modules within a single agent (intrinsic), or even a hybrid approach.
- Key building blocks: LLMs are revolutionizing core multi-agent functionalities like perception (scene description, information gathering), planning (language-based plans, task allocation), communication (zero-shot dialogue, hierarchical message passing), and feedback (plan validation, learning from outcomes, human input).
- Applications: Simulations and initial real-world deployments showcase the potential of LLM-driven multi-agent systems in areas like robotics, intelligent transportation, and embodied question answering.
- Challenges: Standardized evaluation metrics, data limitations, scalability issues, and robust human-robot collaboration frameworks need further research.