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arXiv · 2611.02090

Local Solution Repair for Multi-Agent Pickup and Delivery

Ethan Schneider·Jingkai Chen·Seth Hutchinson·Sonia Chernova
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Abstract

Multi-robot systems in automated warehouses must efficiently attend to a constant stream of pickup-and-delivery tasks while avoiding collisions. Existing Multi-Agent Pickup-and-Delivery (MAPD) solutions generate plans that are globally feasible but often locally inefficient. In large-scale, long-horizon deployments, these inefficiencies, such as robots backtracking from target goal locations and idling in congested areas, accumulate over time and degrade performance. This paper contributes Local Solution Repair (LSR), a goal replanning approach that detects suboptimal behaviors at run-time and locally improves plans to reduce conflict between agents. We demonstrate the utility of our approach by repairing solutions generated by state-of-the-art MAPD baselines, M2M and LNS-PBS. Our results in long-horizon warehouse simulations over three environments show upwards of 5.9% improvement in task throughput over current state-of-the-art methods by reducing the number of suboptimal instances by ~36k or by 14.6% with minor additional computation time.

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Ethan Schneider, Jingkai Chen, Seth Hutchinson, Sonia Chernova. 2026-11-01. Local Solution Repair for Multi-Agent Pickup and Delivery. https://arxiv.org/abs/2611.02090

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