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dc.contributor.authorTekden, A. E.
dc.contributor.authorUgur, E.
dc.contributor.authorNagai, Y.
dc.contributor.authorÖztop, Erhan
dc.date.accessioned2020-04-20T10:45:05Z
dc.date.available2020-04-20T10:45:05Z
dc.date.issued2018-09
dc.identifier.isbn978-1-5386-6110-9
dc.identifier.issn2161-9484en_US
dc.identifier.urihttp://hdl.handle.net/10679/6525
dc.identifier.urihttps://ieeexplore.ieee.org/document/8761045
dc.description.abstractLittle is known about the computational mechanisms of how imitation skills develop along with infant sensorimotor learning. In robotics, there are several well developed frameworks for imitation learning or so called learning by demonstration. Two paradigms dominate: Direct Learning (DL) and Inverse Reinforcement Learning (IRL). The former is a simple mechanism where the observed state and action pairs are associated to construct a copy of the action policy of the demonstrator. In the latter, an optimality principle or reward structure is sought that would explain the observed behavior as the optimal solution governed by the optimality principle or the reward function found. In this study, we explore the plausibility of whether some form of IRL mechanism in infants can facilitate imitation learning and understanding of others' behaviours. We propose that infants project the events taking place in the environment into their internal representations through a set of features that evolve during development. We implement this idea on a grid world environment, which can be considered as a simple model for reaching with obstacle avoidance. The observing infant has to imitate the demonstrator's reaching behavior through IRL by using various set of features that correspond to different stages of development. Our simulation results indicate that the U-shape performance change during imitation development observed in infants can be reproduced with the proposed model.en_US
dc.description.sponsorshipBogazici Resarch Fund (BAP) Startup project ; Slovenia/ARRS -Turkey/TUBITAK bilateral collaboration grant (ARRS Project) ; TÜBİTAK ; JST CREST Cognitive Mirroring, Japan
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relationinfo:turkey/grantAgreement/TUBITAK/215E271
dc.relation.ispartof2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)
dc.rightsrestrictedAccess
dc.titleModeling the development of infant imitation using inverse reinforcement learningen_US
dc.typeConference paperen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-3051-6038 & YÖK ID 45227) Öztop, Erhan
dc.contributor.ozuauthorÖztop, Erhan
dc.identifier.startpage155en_US
dc.identifier.endpage160en_US
dc.identifier.wosWOS:000492050700023
dc.identifier.doi10.1109/DEVLRN.2018.8761045en_US
dc.subject.keywordsObserversen_US
dc.subject.keywordsTask analysisen_US
dc.subject.keywordsReinforcement learningen_US
dc.subject.keywordsTrajectoryen_US
dc.subject.keywordsRobot sensing systemsen_US
dc.subject.keywordsEntropyen_US
dc.identifier.scopusSCOPUS:2-s2.0-85070382645
dc.contributor.authorMale1
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff


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