Sho'ji Suzuki

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Games and Conferences, was held at the Fifteenth International Joint Conference on Artificial Intelligence. There were two leagues: (1) real robot and (2) simulation. Ten teams participated in the realrobot league and 29 teams in the simulation league. Over 150 researchers attended the technical workshop. The world champions are CMUNITED (Carnegie Mellon(More)
This paper presents an approach we 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 informa tion about their current state and their future actions This leads to a generic framework which can be applied to 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 physical bodies play a signi cant role in realizing intelligent(More)
efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. The most significant features of our team, TRACKIES, are the application of a reinforcement learning method enhanced for real robot(More)
  • Sho'ji Suzuki
  • 2011 IEEE International Conference on Robotics…
  • 2011
We propose a vision system consists of two cameras, a camera with wide-lens and an omni-directional camera, to acquire an image useful for remote control of mobile robots. Two camera images are combined to enlarge the field of view in horizontal and vertical. In addition, markers fixed on the robot can be observed in the bottom part of the acquired image(More)
The authors propose an attention control method for an omnidirectional 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)