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  Local Gaussian Processes Regression for Real-time Model-based Robot Control

Nguyen-Tuong, D., & Peters, J. (2008). Local Gaussian Processes Regression for Real-time Model-based Robot Control. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 380-385). Piscataway, NJ, USA: IEEE.

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 Creators:
Nguyen-Tuong, D1, 2, Author              
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: High performance and compliant robot control require accurate dynamics models which 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. This approach offers a natural framework to incorporate unknown nonlinearities as well as to continually adapt online for changes in the robot dynamics. 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. Inspired by locally linear regression techniques, we propose an approximation to the standard GPR using local Gaussian processes models. 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, nu-SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR close to the performance of standard GPR and nu-SVR while being sufficiently fast for online learning.

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 Dates: 2008-09
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/IROS.2008.4650850
BibTex Citekey: 5413
 Degree: -

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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)
Place of Event: Nice, France
Start-/End Date: 2008-09-22 - 2008-09-26

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Title: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 380 - 385 Identifier: ISBN: 978-1-4244-2058-2