State Estimation of a Spinning Ball Using LWR (Locally Weighted Regression)

This paper describes a learning system that has the ability to estimate the flight trajectory of a spinning ball. This system plays an important role in realizing a robot that plays ping-pong against a human. LWR (Locally Weighted Regression) is employed to learn a forward map from stereo image inputs of an incoming ball to the state outputs of the ball just before hitting. The experimental results show that the ball trajetory can be estimated accurately irrespective of how the ball is spinning.

[1]  S. Schaal,et al.  Robot juggling: implementation of memory-based learning , 1994, IEEE Control Systems.

[2]  M.T. Mason,et al.  Dynamic manipulation , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[3]  Daniel E. Koditschek,et al.  Further progress in robot juggling: the spatial two-juggle , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[4]  R. Tibshirani,et al.  Linear Smoothers and Additive Models , 1989 .

[5]  Yasuhiro Masutani,et al.  Learning the inverse map for a robot hitting task , 1995, Adv. Robotics.

[6]  Jean-Jacques E. Slotine,et al.  Space-frequency localized basis function networks for nonlinear system estimation and control , 1995, Neurocomputing.

[7]  Lennart Ljung,et al.  Theory and Practice of Recursive Identification , 1983 .