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 idle time of the system by modeling the operator as a shared resource. In particular, we consider a balking 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.

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Autonomous Robots
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Raeissi, M., & Farinelli, A. (2019). Cooperative queuing policies for effective scheduling of operator intervention. Autonomous Robots, 44, 617–626. doi:10.1007/s10514-019-09877-w