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dc.contributor.authorAcevedo Valle, Juan Manuel
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.authorMoulin-Frier, Clément
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2018-03-20T14:09:49Z
dc.date.available2018-03-20T14:09:49Z
dc.date.issued2017-04-28
dc.identifier.citationAcevedo-Valle, J. M., Angulo, C., Moulin-Frier, C. Autonomous Discovery of Motor Constraints in an Intrinsically-Motivated Vocal Learner. "IEEE Transactions on Cognitive and Developmental Systems", 28 Abril 2017.
dc.identifier.issn2379-8920
dc.identifier.urihttp://hdl.handle.net/2117/115453
dc.description.abstractThis work introduces new results on the modeling of early-vocal development using artificial intelligent cognitive architectures and a simulated vocal tract. The problem is addressed using intrinsically-motivated learning algorithms for autonomous sensorimotor exploration, a kind of algorithm belonging to the active learning architectures family. The artificial agent is able to autonomously select goals to explore its own sensorimotor system in regions where its competence to execute intended goals is improved. We propose to include a somatosensory system to provide a proprioceptive feedback signal to reinforce learning through the autonomous discovery of motor constraints. Constraints are represented by a somatosensory model which is unknown beforehand to the learner. Both the sensorimotor and somatosensory system are modeled using Gaussian mixture models. We argue that using an architecture which includes a somatosensory model would reduce redundancy in the sensorimotor model and drive the learning process more efficiently than algorithms taking into account only auditory feedback. The role of this proposed system is to predict whether an undesired collision within the vocal tract under a certain motor configuration is likely to occur. Thus, compromised motor configurations are rejected, guaranteeing that the agent is less prone to violate its own constraints.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.lcshAutonomous robots
dc.subject.lcshSpeech
dc.subject.lcshMachine learning
dc.subject.otherActive learning
dc.subject.otherearly vocal development
dc.subject.otherGaussina mixture models (GMMs)
dc.subject.otherintrinsic motivations
dc.subject.othersensorimotor exploration
dc.titleAutonomous Discovery of Motor Constraints in an Intrinsically-Motivated Vocal Learner
dc.typeArticle
dc.subject.lemacRobots autònoms
dc.subject.lemacParla
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.identifier.doi10.1109/TCDS.2017.2699578
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7914655/
dc.rights.accessOpen Access
drac.iddocument21145603
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/6PN/TIN2012-38416-C03-01
upcommons.citation.authorAcevedo-Valle, J. M.; Angulo, C.; Moulin-Frier, C.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameIEEE Transactions on Cognitive and Developmental Systems


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