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In the context of the humanoid robotics soccer, ball dribbling is a complex and challenging behavior that requires a proper interaction of the robot with the ball and the floor. We propose a methodology for modeling this behavior by splitting it in two sub problems: alignment and ball pushing. Alignment is achieved using a fuzzy controller in conjunction with an automatic foot selector. Ball-pushing is achieved using a reinforcement-learning based controller, which learns how to keep the robot near the ball, while controlling its speed when approaching and pushing the ball. Four different models for the reinforcement learning of the ball-pushing behavior are proposed and compared. The entire dribbling engine is tested using a 3D simulator and real NAO robots. Performance indices for evaluating the dribbling speed and ball-control are defined and measured. The obtained results validate the usefulness of the proposed methodology, showing asymptotic convergence in around fifty training episodes, and similar performance between simulated and real robots.

Ball Dribbling for Humanoid Biped Robots: A Reinforcement Learning and Fuzzy Control Approach