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This research addresses the critical gap between high-level semantic planning by LLMs and low-level physical feasibility in multi-robot systems. By introducing a geometric verifier that mediates coordination between decentralized agents, the study demonstrates how to resolve inter-robot conflicts without relying on a single centralized planner. The work provides essential insights into balancing computational efficiency with real-world executability for autonomous robot teams.
Verifier-Mediated Multi-LLM Coordination for Multi-Robot Path Planning
arXiv:2610.12761v1 Announce Type: new 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.
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.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2610.12761 [cs.RO]
(or arXiv:2610.12761v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.12761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://openreview.net/group?id=IEEE.org/IROS/2026/Workshop/FM-MRS#tab-recent-activity; https://zhourobotics.github.io/fm-mrs/
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