The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts

TL;DR

This paper explores the potential of learning-based methods to generate scalable, near-optimal solutions for large multi-robot planning problems by transferring offline-learned policies from small-scale systems.

Contribution

It discusses the key challenges and potential strategies for leveraging offline-optimal expert data to improve online multi-robot planning scalability.

Findings

Identifies challenges in transferring offline learning to large-scale systems

Highlights the importance of scalable policy generation in multi-robot planning

Proposes directions for future research in learning-based multi-robot planning

Abstract

Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive, yet optimal solvers, to an offline learning procedure. Simply put, the idea is to train a policy to copy an optimal pattern generated by a small-scale system, and then transfer that policy to much larger systems, in the hope that the learned strategy scales, while maintaining near-optimal performance. Yet, a number of issues impede us from leveraging this idea to its full potential. This blue-sky paper elaborates some of the key challenges that remain.