LIVE: an architecture for learning from the environment
ACM SIGART Bulletin
RoboCup: The Robot World Cup Initiative
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Basic agents for visual/motor coordination of a mobile robot
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Autonomous Learning from the Environment
Autonomous Learning from the Environment
Preface for Special Section on Integrated Cognitive Architectures
ACM SIGART Bulletin
Design and Evaluation of the T-Team of the University of Tuebingen for RoboCup'98
RoboCup-98: Robot Soccer World Cup II
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Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform realtime visual recognition, navigate in a dynamic field, track moving objects, collaborate with teammates, and hit the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond individual's capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). To build such integrated robots, we should use different approaches from those employed in separate research disciplines. In the 1997 RoboCup competition, the USC/ISI robot team, called Dreamteam, fought hard and won the world championship in the middle-sized robot league. These robots all share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goal-keeper, defender or forward) and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. This paper describes our experiences during the competition as well as our new improvements to the team.