A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning. Figueroa, N. & Billard, A. In Billard, A., Dragan, A., Peters, J., & Morimoto, J., editors, volume 87, of Proceedings of Machine Learning Research, pages 927–946, 29–31 Oct, 2018. PMLR.
A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning [link]Paper  abstract   bibtex   6 downloads  
We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) to 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 framework 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.
@InProceedings{Figueroa:CORL:2018, title = {A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning}, author = {Figueroa, Nadia and Billard, Aude}, pages = {927--946}, year = {2018}, editor = {Aude Billard and Anca Dragan and Jan Peters and Jun Morimoto}, volume = {87}, series = {Proceedings of Machine Learning Research}, address = {}, month = {29--31 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v87/figueroa18a/figueroa18a.pdf}, url = {http://proceedings.mlr.press/v87/figueroa18a.html}, abstract = {We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) to 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 framework 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. } }
Downloads: 6
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021