Cooperative Queuing Policies for Effective Human-Multi-Robot Interaction

TL;DR

This paper introduces a multi-robot learning approach to optimize queue management for human intervention, reducing wait times and improving team efficiency in human-multi-robot systems.

Contribution

It formalizes a decentralized learning framework for robots to cooperatively decide on queue joining, enhancing performance in human-robot interaction scenarios.

Findings

Significant reduction in robot waiting times.

Improved team task completion efficiency.

Method is computationally feasible for real-time applications.

Abstract

We consider multi-robot applications, where a team of robots can ask for the intervention of a human operator to handle difficult situations. As the number of requests grows, team members will have to wait for the operator attention, hence the operator becomes a bottleneck for the system. Our aim in this context is to make the robots learn cooperative strategies to decrease the time spent waiting for the operator. In particular, we consider a queuing model where robots decide whether or not to join the queue and use multi-robot learning to estimate the best cooperative policy. In more detail, we formalize the problem as Decentralized Markov Decision Process and provide a suitable state representation, so to apply an independent learners approach. We evaluate the proposed method in a robotic water monitoring simulation and empirically show that our approach can significantly improve the team performance, while being computationally tractable.