Adding Active Learning to LWR for Ping-Pong Playing Robot

In this brief, we consider the problem of controlling the racket attached to the ping-pong playing robot, so that the incoming ball is returned to a desired position. The maps that are used to calculate the racket's initial parameters are described. They are implemented with the locally weighted regression (LWR). An active learning approach based on the fuzzy cerebellar model articulation controller (FCMAC) is proposed, and then it is added to the LWR, which is regarded as lazy learning. A learning algorithm that is used for updating the experience data in the fuzzy CMAC according to the errors between the actual and desired landing positions is presented. A series of experiments has been performed to demonstrate the applicability of the proposed method.

[1]  De Xu,et al.  Control system design for a 5-DOF table tennis robot , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[2]  Jan Peters,et al.  A biomimetic approach to robot table tennis , 2010, IROS.

[3]  Jan Peters,et al.  Learning table tennis with a Mixture of Motor Primitives , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[4]  Juan A. Méndez,et al.  Ping-pong player prototype , 2003, IEEE Robotics Autom. Mag..

[5]  Zne-Jung Lee,et al.  Robust and fast learning for fuzzy cerebellar model articulation controllers , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[7]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[8]  De Xu,et al.  Visual Measurement and Prediction of Ball Trajectory for Table Tennis Robot , 2010, IEEE Transactions on Instrumentation and Measurement.

[9]  Ahmad B. Rad,et al.  An online learning fuzzy controller , 2003, IEEE Trans. Ind. Electron..

[10]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[11]  T. I. James Tsay,et al.  Self-Learning for a Humanoid Robotic Ping-Pong Player , 2011, Adv. Robotics.

[12]  Chi-Cheng Jou,et al.  A fuzzy cerebellar model articulation controller , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[13]  K. Newell,et al.  The informational role of knowledge of results in motor learning. , 1996, Acta psychologica.

[14]  Fumio Miyazaki,et al.  A learning approach to robotic table tennis , 2005, IEEE Transactions on Robotics.

[15]  Jan Peters,et al.  Reinforcement Learning to Adjust Robot Movements to New Situations , 2010, IJCAI.

[16]  Jan Peters,et al.  A biomimetic approach to robot table tennis , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[18]  Daijin Kim,et al.  A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation , 2002, Fuzzy Sets Syst..