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  Real-Time Local GP Model Learning

Nguyen-Tuong, D., Seeger, M., & Peters, J. (2010). Real-Time Local GP Model Learning. In O. Sigaud, & J. Peters (Eds.), From Motor Learning to Interaction Learning in Robots (pp. 193-207). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C186-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-954D-0
Genre: Book Chapter

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 Creators:
Nguyen-Tuong, D1, 2, Author              
Seeger, M, Author              
Peters, J1, 2, Author              
Affiliations:
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 in robotics, accurate dynamics models are essential. However, in some applications, e.g., in model-based tracking control, precise dynamics models cannot be obtained analytically for sufficiently complex robot systems. In such cases, machine learning offers a promising alternative for approximating the robot dynamics using measured data. However, standard regression methods such as Gaussian process regression (GPR) suffer from high computational complexity which prevents their usage for large numbers of samples or online learning to date. In this paper, we propose an approximation to the standard GPR using local Gaussian processes models inspired by [Vijayakumar et al(2005)Vijayakumar, D’Souza, and Schaal, Snelson and Ghahramani(2007)]. Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. Comparisons with other nonparametric regressions, e.g., standard GPR, support vector regression (SVR) and locally weighted proje ction regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.

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 Dates: 2010-01
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1007/978-3-642-05181-4_9
BibTex Citekey: 6233
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Title: From Motor Learning to Interaction Learning in Robots
Source Genre: Book
 Creator(s):
Sigaud, O, Editor
Peters, J1, 2, Editor            
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
2 Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 193 - 207 Identifier: ISBN: 978-3-642-05181-4

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Title: Studies in Computational Intelligence
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 264 Sequence Number: - Start / End Page: - Identifier: -