Learning to Imitate Spatial Organization in Multi-robot Systems

TL;DR

This paper presents a method to reconstruct and analyze collective behaviors in multi-robot systems using prior demonstrations and generative adversarial imitation learning, without needing access to the original swarm controller.

Contribution

It introduces a novel approach that transforms demonstrations into interaction features for behavior reconstruction, outperforming existing methods in spatial organization tasks.

Findings

Outperforms existing algorithms in spatial organization reconstruction

Enables behavior analysis without access to swarm controllers

Facilitates testing and observation of swarm behaviors

Abstract

Understanding collective behavior and how it evolves is important to ensure that robot swarms can be trusted in a shared environment. One way to understand the behavior of the swarm is through collective behavior reconstruction using prior demonstrations. Existing approaches often require access to the swarm controller which may not be available. We reconstruct collective behaviors in distinct swarm scenarios involving shared environments without using swarm controller information. We achieve this by transforming prior demonstrations into features that describe multi-agent interactions before behavior reconstruction with multi-agent generative adversarial imitation learning (MA-GAIL). We show that our approach outperforms existing algorithms in spatial organization, and can be used to observe and reconstruct a swarm's behavior for further analysis and testing, which might be impractical or undesirable on the original robot swarm.