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Implicit Coordination in Robotic Teams using Learned Prediction Models (bibtex)
  author = {Stulp, Freek and Isik, Michael and Beetz, Michael},
  title = {Implicit Coordination in Robotic Teams using Learned Prediction Models},
  booktitle = {Proceedings of the {IEEE} International Conference on Robotics and
	Automation ({ICRA)}},
  year = {2006},
  pages = {1330--1335},
  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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Last edited 29.01.2013 17:37 by Quirin Lohr