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Learning Table Tennis with a Mixture of Motor Primitives

2010

Conference Paper

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Table tennis is a sufficiently complex motor task for studying complete skill learning systems. It consists of several elementary motions and requires fast movements, accurate control, and online adaptation. To represent the elementary movements needed for robot table tennis, we rely on dynamic systems motor primitives (DMP). While such DMPs have been successfully used for learning a variety of simple motor tasks, they only represent single elementary actions. In order to select and generalize among different striking movements, we present a new approach, called Mixture of Motor Primitives that uses a gating network to activate appropriate motor primitives. The resulting policy enables us to select among the appropriate motor primitives as well as to generalize between them. In order to obtain a fully learned robot table tennis setup, we also address the problem of predicting the necessary context information, i.e., the hitting point in time and space where we want to hit the ball. We show that the resulting setup was capable of playing rudimentary table tennis using an anthropomorphic robot arm.

Author(s): Mülling, K. and Kober, J. and Peters, J.
Journal: Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010)
Pages: 411-416
Year: 2010
Month: December
Day: 0
Publisher: IEEE

Department(s): Empirical Inference
Research Project(s): Robot Skill Learning
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICHR.2010.5686298
Event Name: 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010)
Event Place: Nashville, TN, USA

Address: Piscataway, NJ, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@inproceedings{6745,
  title = {Learning Table Tennis with a Mixture of Motor Primitives},
  author = {M{\"u}lling, K. and Kober, J. and Peters, J.},
  journal = {Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010)},
  pages = {411-416},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = dec,
  year = {2010},
  month_numeric = {12}
}