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

ID: 420027.0, MPI für biologische Kybernetik / Biologische Kybernetik
Learning Robot Dynamics for Computed Torque Control Using Local Gaussian Processes Regression
Authors:Nguyen-Tuong, D.; Peters, J.
Editors:Stoica, A.; Tunstel, E.; Huntsberger, T.; Arslan, T.; Vijayakumar, S.; El-Rayis, A. O.
Date of Publication (YYYY-MM-DD):2008-08
Title of Proceedings:Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS 2008)
Start Page:59
End Page:64
Physical Description:6
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Accurate models of the robot dynamics allow the design of significantly more precise, energy-efficient and more compliant computed torque control for robots. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities such as hydraulic cables, complex friction, or actuator dynamics. In such cases, learning the models from data poses an interesting alternative and estimating the dynamics model using regression techniques becomes an important problem. However, the most accurate regression methods, e.g. Gaussian processes regression (GPR) and support vector regression (SVR), suffer from exceptional high computational complexity which prevents their usage for large numbers of samples or online learning to date. We proposed an approximation to the standard GPR using local Gaussian processes models. Due to reduced computational cost, local Gaussian processes (LGP) is capable for an online learning. Comparisons with other nonparametric regre
ssions, e.g. standard GPR, SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and SVR while being sufficiently fast for online learning.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
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