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Real-Time Local GP Model Learning

2010

Book Chapter

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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.

Author(s): Nguyen-Tuong, D. and Seeger, M. and Peters, J.
Book Title: From Motor Learning to Interaction Learning in Robots
Volume: 264
Pages: 193-207
Year: 2010
Month: January
Day: 0

Series: Studies in Computational Intelligence
Editors: Sigaud, O. and Peters, J.
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Berlin, Germany
DOI: 10.1007/978-3-642-05181-4_9
ISBN: 978-3-642-05181-4
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inbook{6233,
  title = {Real-Time Local GP Model Learning},
  author = {Nguyen-Tuong, D. and Seeger, M. and Peters, J.},
  booktitle = {From Motor Learning to Interaction Learning in Robots},
  volume = {264},
  pages = {193-207},
  series = {Studies in Computational Intelligence},
  editors = {Sigaud, O. and Peters, J.},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = jan,
  year = {2010},
  month_numeric = {1}
}