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

  • Leonardo Leottau
  • Carlos Celemin
  • Javier Ruiz-del-Solar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8992)


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.


Reinforcement learning TSK fuzzy controller Soccer robotics Biped robot NAO Behavior Dribbling 



This work was partially funded by FONDECYT under Project Number 1130153 and the Doctoral program in Electrical Engineering at the Universidad de Chile. D.L. Leottau was funded by CONICYT, under grant: CONICYT-PCHA/Doctorado Nacional/2013-63130183.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leonardo Leottau
    • 1
  • Carlos Celemin
    • 1
  • Javier Ruiz-del-Solar
    • 1
  1. 1.Department of Electrical Engineering and Advanced Mining Technology CenterUniversidad de ChileSantiagoChile

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