Huang, Y, Büchler, D, Koç, O et al. (2 more authors) (2017) Jointly learning trajectory generation and hitting point prediction in robot table tennis. In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). IEEE-RAS 16th International Conference on Humanoid Robots, 15-17 Nov 2016, Cancun, México. IEEE , pp. 650-655.
Abstract
This paper proposes a combined learning framework for a table tennis robot. In a typical robot table tennis setup, a single striking point is predicted for the robot on the basis of the ball's initial state. Subsequently, the desired Cartesian racket state and the desired joint states at the striking time are determined. Finally, robot joint trajectories are generated. Instead of predicting a single striking point, we propose to construct a ball trajectory prediction map, which predicts the ball's entire rebound trajectory using the ball's initial state. We construct as well a robot trajectory generation map, which predicts the robot joint movement pattern and the movement duration using the Cartesian racket trajectories without the need of inverse kinematics, where a correlation function is used to adapt these joint movement parameters according to the ball flight trajectory. With joint movement parameters, we can directly generate joint trajectories. Additionally, we introduce a reinforcement learning approach to modify robot joint trajectories such that the robot can return balls well. We validate this new framework in both the simulated and the real robotic systems and illustrate that a seven degree-of-freedom Barrett WAM robot performs well.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Trajectory; Robots; Kinematics; Correlation; Databases; Learning (artificial intelligence); Predictive models |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 May 2020 14:53 |
Last Modified: | 29 May 2020 10:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/HUMANOIDS.2016.7803343 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160112 |