Reactive-behavior Learning of Quadruped Robots Playing Soccer

The robotic soccer belongs to the class of multi-agent systems and involves many challenging sub-problems. Teams of robotic players have to cooperate in order to put the ball in the opposing goal and at the same time defend their own goal. The paper is concerned with the problem of learning and implementation of reactive behaviors for robotic agents playing soccer. It briefly presents the whole control system designed for the Cerberus’01 Sony legged robot team that participated in RoboCup 2001 competitions in Seattle, USA, and then introduces the developed reactive behavior for interception of the moving ball while avoiding collisions with other robotic players and play field walls. For the implementation of the above behavior a fuzzy-neural trajectory generator (FNTG) has been developed and learned. Genetic Algorithms (GAs) based approach has been employed to perform the learning process of the proposed FNTG.