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arXiv · 2610.12761

Verifier-Mediated Multi-LLM Coordination for Multi-Robot Path Planning

Vivek Khatana·Zijian Song·Naira Hovakimyan·Petros Voulgaris
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Abstract

Foundation models are increasingly used for high-level multi-robot task planning, yet independently generated symbolic plans can become jointly infeasible after geometric grounding because of shared spatial and temporal constraints. We study execution-aware coordination of large-language-model (LLM) planners for multi-robot navigation. We compare a centralized architecture, in which one LLM jointly reasons over the robot team, with a decentralized multi-LLM architecture in which each robot independently proposes a task plan. A geometric verifier grounds the candidate plans into robot trajectories, detects predicted inter-robot conflicts, and converts conflict information into structured feedback for targeted replanning. This verifier-mediated architecture separates semantic task reasoning from execution-level feasibility while enabling iterative coordination among LLM planners. Experiments in CARLA and on Crazyflie aerial robots evaluate coordination performance as task complexity increases and characterize the trade-offs between centralized reasoning, decentralized verifier-mediated coordination, and physical executability.

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Vivek Khatana, Zijian Song, Naira Hovakimyan, Petros Voulgaris. 2026-10-08. Verifier-Mediated Multi-LLM Coordination for Multi-Robot Path Planning. https://arxiv.org/abs/2610.12761

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