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  Switched Latent Force Models for Movement Segmentation

Alvarez, M., Peters, J., Schölkopf, B., & Lawrence, N. (2011). Switched Latent Force Models for Movement Segmentation. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, 55-63.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB86-F Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB87-D
Genre: Conference Paper

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 Creators:
Alvarez, MA1, Author           
Peters, J1, 2, Author           
Schölkopf, B1, Author           
Lawrence, ND, Author
Lafferty, Editor
J., Editor
Williams, C. K.I., Editor
Shawe-Taylor, J., Editor
Zemel, R. S., Editor
Culotta, A., Editor
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: Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function. Each variable to be modeled is represented as the output of a differential equation and each differential equation is driven by a weighted sum of latent functions with uncertainty given by a Gaussian process prior. In this paper we consider employing the latent force model framework for the problem of determining robot motor primitives. To deal with discontinuities in the dynamical systems or the latent driving force we introduce an extension of the basic latent force model, that switches between different latent functions and potentially different dynamical systems. This creates a versatile representation for robot movements that can capture discrete changes and non-linearities in the dynamics. We give illustrative examples on both synthetic data and for striking movements recorded using a BarrettWAM robot as haptic input device. Our inspiration is robot motor primitives, but we expect our model to have wide application for dynamical systems including models for human motion capture data and systems biology.

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 Dates: 2011-06
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-1-617-82380-0
URI: http://nips.cc/Conferences/2010/
BibTex Citekey: 6743
 Degree: -

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Title: Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010
Source Genre: Journal
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Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 55 - 63 Identifier: -