Learning stable dynamical systems using contraction theory


This paper discusses the learning of robot point-to-point motions via non-linear dynamical systems and Gaussian Mixture Regression (GMR). The novelty of the proposed approach consists in guaranteeing the stability of a learned dynamical system via Contraction theory. A contraction analysis is performed to derive sufficient conditions for the global stability of a dynamical system represented by GMR. The results of this analysis are exploited to automatically compute a control input which stabilizes the learned system on-line. Simple and effective solutions are proposed to generate motion trajectories close to the demonstrated ones, without affecting the stability of the overall system. The proposed approach is evaluated on a public benchmark of point-to-point motions and compared with state-of-the-art algorithms based on Lyapunov stability theory.

DOI: 10.1109/URAI.2017.7992901

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@article{Blocher2017LearningSD, title={Learning stable dynamical systems using contraction theory}, author={Caroline Blocher and Matteo Saveriano and Dongheui Lee}, journal={2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)}, year={2017}, pages={124-129} }