Learning adjectives and nouns from affordances on the iCub humanoid robot

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dc.citation.epage340en_US
dc.citation.spage330en_US
dc.citation.volumeNumber7426en_US
dc.contributor.authorYürüten O.en_US
dc.contributor.authorUyanık, Fıraten_US
dc.contributor.authorÇalişkan, Y.en_US
dc.contributor.authorBozcuoǧlu, A.K.en_US
dc.contributor.authorŞahin, E.en_US
dc.contributor.authorKalkan, Sinanen_US
dc.coverage.spatialOdense, Denmarken_US
dc.date.accessioned2016-02-08T12:12:58Z
dc.date.available2016-02-08T12:12:58Z
dc.date.issued2012en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: 12th International Conference on Simulation of Adaptive Behavior, SAB 2012en_US
dc.descriptionDate of Conference: August 27-30, 2012en_US
dc.description.abstractThis article studies how a robot can learn nouns and adjectives in language. Towards this end, we extended a framework that enabled robots to learn affordances from its sensorimotor interactions, to learn nouns and adjectives using labeling from humans. Specifically, an iCub humanoid robot interacted with a set of objects (each labeled with a set of adjectives and a noun) and learned to predict the effects (as labeled with a set of verbs) it can generate on them with its behaviors. Different from appearance-based studies that directly link the appearances of objects to nouns and adjectives, we first predict the affordances of an object through a set of Support Vector Machine classifiers which provided a functional view of the object. Then, we learned the mapping between these predicted affordance values and nouns and adjectives. We evaluated and compared a number of different approaches towards the learning of nouns and adjectives on a small set of novel objects. The results show that the proposed method provides better generalization than the appearance-based approaches towards learning adjectives whereas, for nouns, the reverse is the case. We conclude that affordances of objects can be more informative for (a subset of) adjectives describing objects in language. © 2012 Springer-Verlag.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:12:58Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2012en
dc.identifier.doi10.1007/978-3-642-33093-3_33en_US
dc.identifier.doi10.1007/978-3-642-33093-3en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/28167
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-642-33093-3_33en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-642-33093-3en_US
dc.source.titleFrom Animals to Animats 12en_US
dc.subjectAffordancesen_US
dc.subjectAppearance baseden_US
dc.subjectHumanoid roboten_US
dc.subjectAnthropomorphic robotsen_US
dc.subjectHuman robot interactionen_US
dc.titleLearning adjectives and nouns from affordances on the iCub humanoid roboten_US
dc.typeConference Paperen_US

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