Skill learning from human demonstrations using dynamical regressive models for multitask applications Dutta, Samrat, Behera, Laxmidhar and Nahavandi, Saeid 2018, Skill learning from human demonstrations using dynamical regressive models for multitask applications, IEEE transactions on systems, man, and cybernetics: systems, pp. 1-14, doi: 10.1109/TSMC.2018.2885481. Attached Files Name Description MIMEType Size Downloads Title Skill learning from human demonstrations using dynamical regressive models for multitask applications Author(s) Dutta, Samrat Behera, Laxmidhar Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name IEEE transactions on systems, man, and cybernetics: systems Start page 1 End page 14 Total pages 14 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Publication date 2018-12-24 ISSN 2168-2216 2168-2232 Keyword(s) Dynamical systems Learning by demonstration Lyapunov function Regressive models Skill transfer Notes In press Language eng DOI 10.1109/TSMC.2018.2885481 Indigenous content off HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2018, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30118457 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 18 Feb 2019, 12:26:16 EST Mon, 18 Feb 2019, 13:39:42 EST Tue, 25 Jun 2019, 06:01:55 EST Thu, 27 Jun 2019, 14:52:31 EST Thu, 27 Jun 2019, 14:54:13 EST Fri, 20 Sep 2019, 16:24:24 EST Fri, 20 Dec 2019, 12:38:41 EST Mon, 23 Mar 2020, 10:53:51 EST Wed, 24 Jun 2020, 12:00:28 EST Thu, 24 Sep 2020, 14:54:09 EST Sat, 06 Mar 2021, 01:43:02 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 224 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 18 Feb 2019, 12:26:16 EST

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