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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. Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), 380-385.

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 Creators:
Nguyen-Tuong, D1, Author           
Peters, J1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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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: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://www.iros.org/2008
DOI: 10.1109/IROS.2008.4650850
BibTex Citekey: 5413
 Degree: -

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Title: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Place of Event: Nice, France
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Title: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)
Source Genre: Journal
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Publ. Info: Piscataway, NJ, USA : IEEE Service Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 380 - 385 Identifier: -