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Switched Latent Force Models for Movement Segmentation

Mauricio A. Álvarez; Jan Peters; Bernhard Schölkopf; Neil D. Lawrence
In: John D. Lafferty; Christopher K. I. Williams; John Shawe-Taylor; Richard S. Zemel; Aron Culotta (Hrsg.). Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Neural Information Processing Systems (NeurIPS-2010), December 6-9, Vancouver, British Columbia, Canada, Pages 55-63, Curran Associates, Inc. 2010.


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 Barrett WAM 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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