让机器人用对话解决拥堵冲突，提升导航效率与优先级响应
GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments
- 机器人通过自然语言协商冲突，实现自主安全通行
- 在交叉路口使全员到达时间减少超35%，高优先级任务成功率翻倍至100%
- 适用于多智能体协作或自利场景，支持任意数量机器人
在拥挤受限环境中，实现安全、敏捷且符合社交规范的多机器人导航仍是重大挑战，尤其在去中心化场景下，各智能体具有未知且独特的优先级，空间对称性引发冲突难以化解。本文提出GameChat方法，让智能体通过自然语言自主协商，如同人类交流般解决路径冲突，实现无死锁、高效的导航。在含门道与交叉口的仿真环境中评估表明，即使最坏情况下，GameChat相较基线方法将全体智能体抵达目标时间缩短35%以上，较先进基线缩短20%以上；同时，高优先级任务智能体优先通过的比例从50%（随机水平）提升至100%。此外，该方法可扩展至多于两个智能体的场景。
原文摘要 · Abstract (English)
Safe, agile, and socially compliant multi-robot navigation in cluttered and constrained environments remains a critical challenge. This is especially difficult with self-interested agents with unique, unknown priorities in decentralized settings, where there is no central authority to resolve conflicts induced by spatial symmetry. We address this challenge by proposing an intuitive, but very effective approach, GameChat, which facilitates safe, agile, and deadlock-free navigation for both cooperative and self-interested agents in cluttered environments. Key to our approach is the idea that agents should resolve conflicts on their own using natural language to communicate, much like humans. We evaluate GameChat in simulated environments with doorways and intersections. The results show that even in the worst case, GameChat reduces the time for all agents to reach their goals by over 35% from a naive baseline and by over 20% from a state of the art baseline in the intersection scenario, while doubling the rate of ensuring the agent with a higher priority task reaches the goal first, from 50% (equivalent to random chance) to 100%. We also demonstrate how GameChat can be extended to more than two agents.
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