Publication Details

SELECT * FROM publications WHERE Record_Number=11377
Reference TypeConference Proceedings
Author(s)Urain, J.; Ginesi, M.; Tateo, D.; Peters, J.
Year2020
TitleImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
Journal/Conference/Book TitleIEEE/RSJ International Conference on Intelligent Robots and Systems
KeywordsMovement Primitives, Imitation Learning
AbstractWe introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations. We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to learn them from a set of demonstrated trajectories. Our model extends the set of stable dynamical systems that can be represented by state-of-the-art approaches, eliminates the Gaussian assumption on the demonstrations, and outperforms the previous algorithms in terms of representation accuracy. We show the effectiveness of our method with both standard datasets and a real robot experiment.
Link to PDFhttps://www.ias.informatik.tu-darmstadt.de/uploads/Team/JulenUrainDeJesus/2020iflowurain.pdf

  

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