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Open AccessArticle

Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations

Department of Psychology, University of Marburg, Gutenbergstr. 18, 35032 Marburg, Germany
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 26th International Conference on Artificial Neural Networks (ICANN 2017), Alghero, Italy, 11–14 September, 2017
Entropy 2018, 20(10), 724;
Received: 27 July 2018 / Revised: 20 August 2018 / Accepted: 20 September 2018 / Published: 21 September 2018
We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dynamical model in discrete time. The purpose of the approximation is threefold: first, to reduce training time of the model; second, to enable modular re-use of learned dynamics; and, third, to store these learned dynamics compactly. Our target applications here are human movement primitive (MP) models, where an MP is a reusable spatiotemporal component, or “module” of a human full-body movement. Besides re-usability of learned MPs, compactness is crucial, to allow for the storage of a large library of movements. We first derive the variational approximation, illustrate it on toy data, test its predictions against a range of other MP models and finally compare movements produced by the model against human perceptual expectations. We show that the variational CGPDM outperforms several other MP models on movement trajectory prediction. Furthermore, human observers find its movements nearly indistinguishable from replays of natural movement recordings for a very compact parameterization of the approximation. View Full-Text
Keywords: Gaussian processes; variational methods; movement primitives; modularity Gaussian processes; variational methods; movement primitives; modularity
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Velychko, D.; Knopp, B.; Endres, D. Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations. Entropy 2018, 20, 724.

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