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Learning Complex Motions by Sequencing Simpler Motion Templates

2009

Conference Paper

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Abstraction of complex, longer motor tasks into simpler elemental movements enables humans and animals to exhibit motor skills which have not yet been matched by robots. Humans intuitively decompose complex motions into smaller, simpler segments. For example when describing simple movements like drawing a triangle with a pen, we can easily name the basic steps of this movement. Surprisingly, such abstractions have rarely been used in artificial motor skill learning algorithms. These algorithms typically choose a new action (such as a torque or a force) at a very fast time-scale. As a result, both policy and temporal credit assignment problem become unnecessarily complex - often beyond the reach of current machine learning methods. We introduce a new framework for temporal abstractions in reinforcement learning (RL), i.e. RL with motion templates. We present a new algorithm for this framework which can learn high-quality policies by making only few abstract decisions.

Author(s): Neumann, G. and Maass, W. and Peters, J.
Book Title: ICML 2009
Journal: Proceedings of the 26th International Conference on Machine Learning (ICML 2009)
Pages: 753-760
Year: 2009
Month: June
Day: 0
Editors: Danyluk, A. , L. Bottou, M. Littman
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1553374.1553471
Event Name: 26th International Conference on Machine Learning
Event Place: Montreal, Canada

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5880,
  title = {Learning Complex Motions by Sequencing Simpler Motion Templates},
  author = {Neumann, G. and Maass, W. and Peters, J.},
  journal = {Proceedings of the 26th International Conference on Machine Learning (ICML 2009)},
  booktitle = {ICML 2009},
  pages = {753-760},
  editors = {Danyluk, A. , L. Bottou, M. Littman},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2009},
  month_numeric = {6}
}