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

ABSTRACT: 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

SUBMITTER: Velychko D 

PROVIDER: S-EPMC7512289 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature