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

ID: 461695.0, MPI für biologische Kybernetik / Biologische Kybernetik
Model Learning with Local Gaussian Process Regression
Authors:Nguyen-Tuong, D.; Seeger, M.; Peters, J.
Date of Publication (YYYY-MM-DD):2009-11
Title of Journal:Advanced Robotics
Issue / Number:15
Start Page:2015
End Page:2034
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Precise models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time model online learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive learning performance for hig
h-dimensional data while being sufficiently fast for real-time learning. The effectiveness of LGP is exhibited by a comparison with the state-of-the-art regression techniques, such as GPR, LWPR and ν-support vector regression. The applicability of the proposed LGP method is demonstrated by real-time online learning of the inverse dynamics model for robot model-based control on a Barrett WAM robot arm.
External Publication Status:published
Document Type:Article
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
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