Computational model of a mirror neuron system for intent recognition through imitative learning of objective-directed action
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ko, K.-E. | - |
dc.contributor.author | Sim, K.-B. | - |
dc.date.available | 2019-03-09T00:40:28Z | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/13865 | - |
dc.description.abstract | The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Control, Robotics and Systems | - |
dc.title | Computational model of a mirror neuron system for intent recognition through imitative learning of objective-directed action | - |
dc.type | Article | - |
dc.identifier.doi | 10.5302/J.ICROS.2014.14.9033 | - |
dc.identifier.bibliographicCitation | Journal of Institute of Control, Robotics and Systems, v.20, no.6, pp 606 - 611 | - |
dc.identifier.kciid | ART001880351 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-84924962400 | - |
dc.citation.endPage | 611 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 606 | - |
dc.citation.title | Journal of Institute of Control, Robotics and Systems | - |
dc.citation.volume | 20 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Human-robot interaction | - |
dc.subject.keywordAuthor | Imitative learning | - |
dc.subject.keywordAuthor | Intent recognition | - |
dc.subject.keywordAuthor | Mirror neuron system | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.