[2512.03194] GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding

Computer Science > Robotics arXiv:2512.03194 (cs) [Submitted on 2 Dec 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding Authors:Johannes Gaber, Meshal Alharbi, Daniele Gammelli, Gioele Zardini View a PDF of the paper titled GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding, by Johannes Gaber and 3 other authors View PDF Abstract:Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task scheduling for lifelong Multi-Agent Pickup-and-Delivery (MAPD) and propose a hybrid method that couples learning-based global guidance with lightweight optimization. A graph neural network policy trained via reinforcement learning outputs a desired distribution of free agents over an aggregated warehouse graph. This signal is converted into region-to-region rebalancing through a minimum-cost flow, and finalized by small, local assignment problems, preserving accuracy while keeping per-step latency within a 1 s compute budget. We call this approach GRAND: a hierarchical algorithm that relies on Guidance, Rebalancing, and Assignment to explicitly leverage the workspace Network structure and Dispatch agents to tasks. On congested warehouse benchmarks from the League of Robot Runners (LoRR) with up to 500 agents, our approach ...