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

2008

Conference Paper

ei


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.

Author(s): Nguyen-Tuong, D. and Peters, J.
Book Title: IROS 2008
Journal: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)
Pages: 380-385
Year: 2008
Month: September
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/IROS.2008.4650850
Event Name: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Event Place: Nice, France

Address: Piscataway, NJ, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5413,
  title = {Local Gaussian Processes Regression for Real-time Model-based Robot Control},
  author = {Nguyen-Tuong, D. and Peters, J.},
  journal = {Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)},
  booktitle = {IROS 2008},
  pages = {380-385},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2008},
  month_numeric = {9}
}