Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning

TL;DR

This paper introduces a decentralized graph neural network approach for unlabeled multi-robot motion planning, demonstrating scalable generalization from 100 to 500 robots and outperforming existing methods.

Contribution

The paper presents a novel GNN-based decentralized policy trained via imitation and reinforcement learning for scalable multi-robot motion planning.

Findings

GNN policy generalizes from 100 to 500 robots.

Outperforms state-of-the-art solutions by 8.6%.

Significantly surpasses greedy decentralized methods.

Abstract

Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance, aiming to minimize the total distance traveled. The problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets. This scenario combines elements of combinatorial assignment and continuous-space motion planning, posing significant scalability challenges for traditional centralized approaches. To overcome these challenges, we propose a decentralized policy learned via a Graph Neural Network (GNN). The GNN enables robots to determine (1) what information to communicate to neighbors and (2) how to integrate received information with local observations for decision-making. We train the GNN using imitation learning with the centralized Hungarian algorithm as the expert policy, and further fine-tune it using reinforcement learning to avoid collisions and enhance performance. Extensive empirical evaluations demonstrate the scalability and effectiveness of our approach. The GNN policy trained on 100 robots generalizes to scenarios with up to 500 robots, outperforming state-of-the-art solutions by 8.6\% on average and significantly surpassing greedy decentralized methods. This work lays the foundation for solving multi-robot coordination problems in settings where scalability is important.