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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorAbu-Dakka, Fares J.
dc.contributor.otherHu, Yingbai
dc.contributor.otherChen, Fei
dc.contributor.otherLuo, Xiao
dc.contributor.otherLi, Zheng
dc.contributor.otherKnoll, Alois
dc.contributor.otherDing, Weiping
dc.date.accessioned2024-04-18T08:51:30Z
dc.date.available2024-04-18T08:51:30Z
dc.date.issued2024
dc.identifier.issn1872-6305en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=176472en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6358
dc.description.abstractImitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers’ comprehension of dynamical systems’ foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2024 Elsevieren
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectImitation learningen
dc.subjectDynamical systemen
dc.subjectFusion of theoretical paradigmsen
dc.subjectStabilityen
dc.subjectPolicy explorationen
dc.titleFusion dynamical systems with machine learning in imitation learning: A comprehensive overviewen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceInformation Fusionen
local.contributor.groupRobótica y automatizaciónes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.inffus.2024.102379en
local.embargo.enddate2026-08-31
local.contributor.otherinstitutionhttps://ror.org/00t33hh48en
local.contributor.otherinstitutionhttps://ror.org/02kkvpp62en
local.contributor.otherinstitutionhttps://ror.org/02afcvw97en
local.source.detailsVol. 108. N. art. 102379. August, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
oaire.funderNameNational Natural Science Foundation of Chinaen
oaire.funderNameNatural Science Foundation of Jiangsu Provinceen
oaire.funderNameNatural Science Key Foundation of Jiangsu Education Departmenten
oaire.funderNameChinese University of Hong Kongen
oaire.funderNameChinese University of Hong Kongen
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno Vascoen
oaire.funderIdentifierhttps://ror.org/01h0zpd94
oaire.funderIdentifierNatural Science Foundation of Jiangsu Province
oaire.funderIdentifierhttps://ror.org/059md9404
oaire.funderIdentifierhttps://ror.org/00t33hh48
oaire.funderIdentifierhttps://ror.org/00t33hh48
oaire.funderIdentifierhttps://ror.org/00pz2fp31
oaire.funderIdentifierhttps://ror.org/00pz2fp31
oaire.fundingStreamSin informaciónen
oaire.fundingStreamSin informaciónen
oaire.fundingStreamSin informaciónen
oaire.fundingStreamResearch Grant Council General Research funden
oaire.fundingStreamThe CUHK Strategic Seed Funding for Collaborative Research scheme 22/21 (SSFCRS)en
oaire.fundingStreamElkartek 2022en
oaire.fundingStreamElkartek 2023en
oaire.awardNumber61976120en
oaire.awardNumberBK20231337en
oaire.awardNumber21KJA510004en
oaire.awardNumber14202820 and 1421432en
oaire.awardNumberSin informaciónen
oaire.awardNumberKK-2022-00024en
oaire.awardNumberKK-2023-00055en
oaire.awardTitleKnowledge Collaborative Reduction Theories and Approaches of Large-scale Electronic Medical Records for Cloud Computingen
oaire.awardTitleMulti-modal big data knowledge discovery model and algorithm based on multi-granularity computingen
oaire.awardTitleResearch on multi-granularity knowledge discovery method and its key technologies of integrated optimization for multi-mode Big Dataen
oaire.awardTitleSin informaciónen
oaire.awardTitleSin informaciónen
oaire.awardTitleProducción Fluída y Resiliente para la Industria inteligente (PROFLOW)en
oaire.awardTitleTecnologías de Inteligencia Artificial para la percepción visual y háptica y la planificación y control de tareas de manipulación (HELDU)en
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