Real-Time Local GP Model Learning

  • Duy Nguyen-Tuong
  • Matthias Seeger
  • Jan Peters
Part of the Studies in Computational Intelligence book series (SCI, volume 264)


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 projection regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.


Root Mean Square Error Tracking Error Gaussian Process Local Model Support Vector Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Duy Nguyen-Tuong
    • 1
  • Matthias Seeger
    • 2
  • Jan Peters
    • 1
  1. 1.Max Planck Institute for Biological CyberneticsTübingen
  2. 2.Saarland UniversitySaarbrücken

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