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This paper presents an approach w e have recently developed for multi-robot cooperation. It is based on a paradigm where robots incrementally merge their plans into a set of already coordinated plans. This is done through exchange of information about their current state and their future actions. This leads to a generic framework which can be applied t o a(More)
Traditional AI research has not given due attention to the important role that physical bodies play for agents as their interactions produce complex emergent behaviors to achieve goals in the dynamic real world. The RoboCup Physical Agent Challenge provides a good testbed for studying how p h ysical bodies play a signiicant role in realizing intelligent(More)
ligence. There were two leagues: (1) real robot and (2) simulation. Ten teams participated in the real-robot league and 29 teams in the simulation league. Over 150 researchers attended the technical workshop. The world champions are CMUNITED (Carnegie Mellon University) for the small-size league, DREAMTEAM (University of Southern Califor-nia) and TRACKIES(More)
The authors propose an attention control method for an omnidi-rectional vision by an active zoom mechanism. It is implemented by controlling focal length of the camera without pan or tilt mechanism. We install an omnidirectional vision with a hyperbolic mirror to a mobile robot and apply Q-learning for its behavior acquisition. In a goal defending behavior(More)
The authors have applied reinforcement learning methods to real robot tasks in several aspects. We selected a skill of soccer as a task for a vision-based mobile robot. In this paper, we explain two of our method; (1)learning a shooting behavior, and (2)learning a shooting with avoiding an opponent. These behaviors were obtained by a robot in simulation and(More)