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Freek Stulp, Michael Isik, and Michael Beetz. Implicit Coordination in Robotic Teams using Learned Prediction Models. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2006.

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Abstract

Many application tasks require the cooperation of two or more robots. Humans are good at cooperation in shared workspaces, because they anticipate and adapt to the intentions and actions of others. In contrast, multi-agent and multi-robot systems rely on communication to exchange their intentions. This causes problems in domains where perfect communication is not guaranteed, such as rescue robotics, autonomous vehicles participating in traffic, or robotic soccer. In this paper, we introduce a computational model for implicit coordination, and apply it to a typical coordination task from robotic soccer: regaining ball possession. The computational model specifies that performance prediction models are necessary for coordination, so we learn them off-line from observed experience. By taking the perspective of the team mates, these models are then used to predict utilities of others, and optimize a shared performance model for joint actions. In several experiments conducted with our robotic soccer team, we evaluate the performance of implicit coordination.

BibTeX Entry

@InProceedings{stulp06implicit,
  author =            {Freek Stulp and Michael Isik and Michael Beetz},
  title =             {Implicit Coordination in Robotic Teams using Learned Prediction Models},
  booktitle =         {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year =              {2006},
  bib2html_pubtype =  {Refereed Conference Paper},
  bib2html_rescat =   {Robotics - Motor Control},
  abstract =          {
  Many application tasks require the cooperation of two or more robots. Humans
  are good at cooperation in shared workspaces, because they anticipate and
  adapt to the intentions and actions of others. In contrast, multi-agent and
  multi-robot systems rely on communication to exchange their intentions. This
  causes problems in domains where perfect communication is not guaranteed, such
  as rescue robotics, autonomous vehicles participating in traffic, or robotic
  soccer.
  In this paper, we introduce a computational model for implicit coordination,
  and apply it to a typical coordination task from robotic soccer: regaining
  ball possession. The computational model specifies that performance prediction
  models are necessary for coordination, so we learn them off-line from observed
  experience. By taking the perspective of the team mates, these models are then
  used to predict utilities of others, and optimize a shared performance model
  for joint actions. In several experiments conducted with our robotic soccer
  team, we evaluate the performance of implicit coordination.
  }
}

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