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Local Gaussian Process Regression for Real Time Online Model Learning and Control

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84108

Nguyen-Tuong,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Nguyen-Tuong, D., Seeger, M., & Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control. Advances in neural information processing systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008, 1193-1200.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C493-C
Abstract
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The prediction for a query point is performed by weighted estimation using nearby local models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online learning and prediction in real-time. Comparisons with other nonparametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and nu-SVR.