Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot Systems

arXiv:2404.06413v1 Announce Type: cross
Abstract: Multi-agent robotic systems are prone to deadlocks in an obstacle environment where the system can get stuck away from its desired location under a smooth low-level control policy. Without an external intervention, often in terms of a high-level command, it is not possible to guarantee that just a low-level control policy can resolve such deadlocks. Utilizing the generalizability and low data requirements of large language models (LLMs), this paper explores the possibility of using LLMs …

abstract agent arxiv command control cs.cl cs.ro deadlock environment language language models large language large language models location low math.oc multi-agent policy resolution robot robotic systems terms type