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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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