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          Institute: MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung)     Collection: Abt. Schölkopf (Empirical Inference)     Display Documents



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ID: 596845.0, MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung) / Abt. Schölkopf (Empirical Inference)
Incremental online sparsification for model learning in real-time robot control
Authors:Nguyen-Tuong, D.; Peters, J.
Date of Publication (YYYY-MM-DD):2011-05-01
Title of Journal:Neurocomputing
Volume:74
Issue / Number:11
Start Page:1859
End Page:1867
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:For many applications such as compliant, accurate robot tracking control, dynamics models learned from data can help to achieve both compliant control performance as well as high tracking quality. Online learning of these dynamics models allows the robot controller to adapt itself to changes in the dynamics (e.g., due to time-variant nonlinearities or unforeseen loads). However, online learning in real-time applications -- as required in control -- cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time model learning. The proposed approach employs a sparsification method based on an independence measure. In combination with an incremental learning approach such as incremental Gaussian process regression, we obtain a model approximation method which is applicable in real-time online learning.
It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real Barrett WAM robot demonstrates the applicability of the approach in real-time online model learning for real world systems.
External Publication Status:published
Document Type:Article
Communicated by:Heide Klooz
Affiliations:MPI für Intelligente Systeme/Abt. Schölkopf
Identifiers:URL:http://www.kyb.tuebingen.mpg.de/
LOCALID:6650
DOI:10.1016/j.neucom.2010.06.033
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