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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



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ID: 548537.0, MPI für biologische Kybernetik / Biologische Kybernetik
Real-Time Local GP Model Learning
Authors:Nguyen-Tuong, D.; Seeger, M.; Peters, J.
Editors:Sigaud, O.; Peters, J.
Place of Publication:Berlin, Germany
Publisher:Springer
Date of Publication (YYYY-MM-DD):2010-01
Title of Book:From Motor Learning to Interaction Learning in Robots
Start Page:193
End Page:207
Physical Description:15
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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, De 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.
External Publication Status:published
Document Type:InBook
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:URL:http://springerlink.com/content/r4j26718025757n2/?...
LOCALID:6233
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