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Home Search Quick Search Advanced Fulltext Browse Collections Persons My eDoc Session History Login Name: Password: Documentation Help Support Wiki Direct access to document ID:	Institute: MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung)     Collection: Abt. Schölkopf (Empirical Inference)     Display Documents   history 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 The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.