How can LLMs improve multi-robot navigation?
Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation
This paper introduces SAMALM, a decentralized multi-agent framework using LLMs for controlling multiple robots navigating a space with humans. Each robot has an individual LLM "actor" generating control signals and "critic" LLMs providing feedback, alongside a global "critic" LLM evaluating overall group behavior. This actor-critic system, enhanced by entropy-based score fusion for verification and re-querying, aims for robust and socially-compliant navigation, addressing limitations of centralized LLM approaches in multi-robot systems by considering individual robot characteristics and preferences. Key aspects relevant to LLM-based multi-agent systems include the decentralized architecture, the personalized prompts for individual LLM actors, the two-tiered critic system combining individual and global feedback, and the use of entropy for score fusion to ensure robust and adaptable behavior.