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Learning Anticipation Policies for Robot Table Tennis

Wang, Zhikun and Lampert, Christoph H. and Mülling, Katharina and Schölkopf, Bernhard and Peters, Jan (2011):
Learning Anticipation Policies for Robot Table Tennis.
In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, IEEE, pp. 332-337, [Book Section]

Abstract

Playing table tennis is a difficult task for robots, especially due to their limitations of acceleration. A key bottleneck is the amount of time needed to reach the desired hitting position and velocity of the racket for returning the incoming ball. Here, it often does not suffice to simply extrapolate the ball's trajectory after the opponent returns it but more information is needed. Humans are able to predict the ball's trajectory based on the opponent's moves and, thus, have a considerable advantage. Hence, we propose to incorporate an anticipation system into robot table tennis players, which enables the robot to react earlier while the opponent is performing the striking movement. Based on visual observation of the opponent's racket movement, the robot can predict the aim of the opponent and adjust its movement generation accordingly. The policies for deciding how and when to react are obtained by reinforcement learning. We conduct experiments with an existing robot player to show that the learned reaction policy can significantly improve the performance of the overall system.

Item Type: Book Section
Erschienen: 2011
Creators: Wang, Zhikun and Lampert, Christoph H. and Mülling, Katharina and Schölkopf, Bernhard and Peters, Jan
Title: Learning Anticipation Policies for Robot Table Tennis
Language: English
Abstract:

Playing table tennis is a difficult task for robots, especially due to their limitations of acceleration. A key bottleneck is the amount of time needed to reach the desired hitting position and velocity of the racket for returning the incoming ball. Here, it often does not suffice to simply extrapolate the ball's trajectory after the opponent returns it but more information is needed. Humans are able to predict the ball's trajectory based on the opponent's moves and, thus, have a considerable advantage. Hence, we propose to incorporate an anticipation system into robot table tennis players, which enables the robot to react earlier while the opponent is performing the striking movement. Based on visual observation of the opponent's racket movement, the robot can predict the aim of the opponent and adjust its movement generation accordingly. The policies for deciding how and when to react are obtained by reinforcement learning. We conduct experiments with an existing robot player to show that the learned reaction policy can significantly improve the performance of the overall system.

Title of Book: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Place of Publication: San Francisco
Publisher: IEEE
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
Event Title: IEEE/RSJ International Conference on Intelligent Robot Systems (IROS)
Event Location: San Francisco, CA
Event Dates: 25-30 September 2011
Date Deposited: 29 Nov 2011 13:57
Additional Information:

Intelligent Autonomous Systems

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