Peter Stone's Selected Publications

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Leading the Way: An Efficient Multi-robot Guidance System

Piyush Khandelwal, Samuel Barrett, and Peter Stone. Leading the Way: An Efficient Multi-robot Guidance System. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.

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Abstract

Recent advances in service robotics have made it possible to deploy a large number of mobile robots in indoor environments to perform tasks such as delivery, maintenance and eldercare. If a centrally connected multi-robot system is available, can it be effectively used to aid humans in other on-demand tasks? In this paper, we demonstrate how individual service robots in a multi-robot system can be temporarily reassigned from their original task to help guide a human from one location to another in the environment. We formulate this multi-robot treatment of the human guidance problem as a Markov Decision Process (MDP). Solving the MDP produces a policy to efficiently guide the human, but the state space size makes it infeasible to optimally solve it. Instead, we use the Upper Confidence bound for Trees (UCT) planner to obtain an approximate solution. We show that this solution outperforms an approach that uses a single robot to guide the human from start to finish.

BibTeX Entry

@InProceedings{AAMAS15-khandelwal,
  author = {Piyush Khandelwal and Samuel Barrett and Peter Stone},
  title = {Leading the Way: An Efficient Multi-robot Guidance System},
  booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  location = {Istanbul, Turkey},
  month = {May},
  year = {2015},
  abstract = {
    Recent advances in service robotics have made it possible to deploy a large
      number of mobile robots in indoor environments to perform tasks such as
      delivery, maintenance and eldercare. If a centrally connected multi-robot
      system is available, can it be effectively used to aid humans in other
      on-demand tasks? In this paper, we demonstrate how individual service
      robots in a multi-robot system can be temporarily reassigned from their
      original task to help guide a human from one location to another in the
      environment. We formulate this multi-robot treatment of the human guidance
      problem as a Markov Decision Process (MDP). Solving the MDP produces a
      policy to efficiently guide the human, but the state space size makes it
      infeasible to optimally solve it. Instead, we use the Upper Confidence
      bound for Trees (UCT) planner to obtain an approximate solution. We show
      that this solution outperforms an approach that uses a single robot to
      guide the human from start to finish.
  },
}

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