How can LLMs improve multi-agent navigation safety?
GAMECHAT: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments
March 18, 2025
https://arxiv.org/pdf/2503.12333GAMECHAT uses LLMs to enable multi-robot navigation in tight spaces like doorways and intersections. Robots converse to determine task priority and avoid deadlocks/collisions, mimicking human social interactions. A fallback game-theoretic strategy ensures robust performance even without communication. Key to LLM integration is prioritizing urgent tasks (like navigating to a hospital vs. a grocery store) through natural language dialogue, improving social optimality compared to non-communicative methods. This represents progress toward practical LLM-based multi-agent systems capable of courteous, human-like interactions.