A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control-Reference-Cited by-同舟云学术

A trajectory and force dual-incremental robot skill learning and generalization framework using improved dynamical movement primitives and adaptive neural network control

Published:2023-02 Issue: Volume:521 Page:146-159
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Lu Zhenyu,Wang Ning,Li Qinchuan,Yang Chenguang

Funder

European Commission

Shanxi Provincial Key Research and Development Project

H2020 Marie Skłodowska-Curie Actions

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference38 articles.

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5. M. Saveriano, F.J. Abu-Dakka, A. Kramberger, L. Peternel, Dynamic movement primitives in robotics: A tutorial survey. arXiv preprint arXiv:2102.03861 (2021).

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