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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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 Urheber:
Nguyen-Tuong, D1, 2, Autor           
Seeger, M, Autor           
Peters, J1, 2, Autor           
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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 Zusammenfassung: 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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 Datum: 2010-01
 Publikationsstatus: Erschienen
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 Identifikatoren: DOI: 10.1007/978-3-642-05181-4_9
BibTex Citekey: 6233
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Titel: From Motor Learning to Interaction Learning in Robots
Genre der Quelle: Buch
 Urheber:
Sigaud, O, Herausgeber
Peters, J1, 2, Herausgeber           
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            
Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 193 - 207 Identifikator: ISBN: 978-3-642-05181-4

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Titel: Studies in Computational Intelligence
Genre der Quelle: Reihe
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 264 Artikelnummer: - Start- / Endseite: - Identifikator: -