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  Incremental online sparsification for model learning in real-time robot control

Nguyen-Tuong, D., & Peters, J. (2011). Incremental online sparsification for model learning in real-time robot control. Neurocomputing, 74(11), 1859-1867. doi:10.1016/j.neucom.2010.06.033.

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Nguyen-Tuong, D1, 2, Author              
Peters, J1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: For many applications such as compliant, accurate robot tracking control, dynamics models learned from data can help to achieve both compliant control performance as well as high tracking quality. Online learning of these dynamics models allows the robot controller to adapt itself to changes in the dynamics (e.g., due to time-variant nonlinearities or unforeseen loads). However, online learning in real-time applications -- as required in control -- cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time model learning. The proposed approach employs a sparsification method based on an independence measure. In combination with an incremental learning approach such as incremental Gaussian process regression, we obtain a model approximation method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real Barrett WAM robot demonstrates the applicability of the approach in real-time online model learning for real world systems.

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 Dates: 2011-05
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neucom.2010.06.033
BibTex Citekey: 6650
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Title: Neurocomputing
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 74 (11) Sequence Number: - Start / End Page: 1859 - 1867 Identifier: ISSN: 0925-2312
CoNE: https://pure.mpg.de/cone/journals/resource/954925566733