pc-gmm-ds-learning

Project page for CoRL 2018 paper on physically consistent GMM and DS learning.

This project is maintained by nbfigueroa

Physically Consistent GMM and DS Learning

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 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.

Video of Approach and Robot Experiments

Code

Following we list all of the code repositories made available for this project, including:

References

[1] Figueroa, N. and Billard, A. (2018) “A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning”. In Proceedings of the 2nd Conference on Robot Learning (CoRL).

Contact

Nadia Figueroa (nadia.figueroafernandez AT epfl dot ch)

Acknowledgments

This work was supported by the EU project Cogimon H2020-ICT-23-2014.