  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 for deadlock resolution. We propose a hierarchical
control framework where an LLM resolves deadlocks by assigning a leader and
direction for the leader to move along. A graph neural network (GNN) based
low-level distributed control policy executes the assigned plan. We
systematically study various prompting techniques to improve LLM's performance
in resolving deadlocks. In particular, as part of prompt engineering, we
provide in-context examples for LLMs. We conducted extensive experiments on
various multi-robot environments with up to 15 agents and 40 obstacles. Our
results demonstrate that LLM-based high-level planners are effective in
resolving deadlocks in MRS.
