Publication

A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning

Aude Billard, Nadia Barbara Figueroa Fernandez
2018
Conference Papers

Abstract

We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) on trajectory data. Physical-consistency of the GMM is ensured by imposing a prior on the component assignments biased by a novel similarity metric that leverages locality and directionality. The resulting GMM is then used to learn globally asymptotically stable Dynamical Systems (DS) via a Linear Parameter Varying (LPV) re-formulation. The proposed DS learning scheme accurately encodes challenging nonlinear motions automatically. Finally, a data-efficient incremental learning approach is introduced that encodes a DS from batches of trajectories, while preserving global stability. Our contributions are validated on 2D datasets and a variety of tasks that involve single-target complex motions with a KUKA LWR 4+ robot arm.

Official source

https://infoscience.epfl.ch/entities/publication/c4827e9c-b9c7-42a7-98f8-dd4ec22bb782

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Aude Billard, Nadia Barbara Figueroa Fernandez
2018
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c4827e9c-b9c7-42a7-98f8-dd4ec22bb782

About this result

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