Learning Stable Movement Primitives by Finding a Suitable Fuzzy Lyapunov Function from Kinesthetic Demonstrations

Samrat Dutta, Swagat Kumar, Laxmidhar Behera

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Transferring skills to roUots through human demonstrations is an interesting problem. Locally generated demonstrations of reaching motion, given by a human teacher are generally encoded in a dynamical model. Stability of this encoding system demands great attention while learning the model parameters. In that context, we present a new architecture of dynamical system to learn movement primitives from multiple demonstrations exploiting a fuzzy Lyapunov function (FLF). We assume that there exists a natural Lyapunov function (LF) that associates the demonstrations. The proposed FLF tries to approximate that LF. First, the dynamics of the demonstrations are encoded in a regressive model, learnt using Gaussian mixture regression with EM algorithm. Then the FLF is searched involving the learnt dynamics in an optimization process. The FLF in turn helps to learn a fuzzy controller. Our architecture is new in a sense that it combines the probabilistic model with a fuzzy controller to create a globally asymptotically stable motion model. The proposed algorithm can simultaneously learn position and orientation profiles in a single model. The algorithm is experimentally validated on a commercially available manipulator and also compared with a state-of-the-art technique.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781509060146
DOIs
Publication statusPublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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    Dutta, S., Kumar, S., & Behera, L. (2018). Learning Stable Movement Primitives by Finding a Suitable Fuzzy Lyapunov Function from Kinesthetic Demonstrations. In Proceedings of the International Joint Conference on Neural Networks (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489055