dc.creatorChauhan A.
dc.creatorNascimento A.
dc.creatorWerneck B.
dc.creatorSeabra Lopes L.
dc.date2009
dc.date2015-06-26T13:36:29Z
dc.date2015-11-26T15:36:45Z
dc.date2015-06-26T13:36:29Z
dc.date2015-11-26T15:36:45Z
dc.date.accessioned2018-03-28T22:45:13Z
dc.date.available2018-03-28T22:45:13Z
dc.identifier3642046851; 9783642046858
dc.identifierLecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 5816 LNAI, n. , p. 263 - 274, 2009.
dc.identifier3029743
dc.identifier10.1007/978-3-642-04686-5_22
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-71049118629&partnerID=40&md5=245baf058509092e1ccb6255f70452ed
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/92558
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/92558
dc.identifier2-s2.0-71049118629
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1263507
dc.descriptionFor robots to interact with humans at the language level, it becomes fundamental that robots and humans share a common language. In this paper, a social language grounding paradigm is adopted to teach a robotic arm basic vocabulary about objects in its environment. A human user, acting as an instructor, teaches the names of the objects present in their shared field of view. The robotic agent grounds these words by associating them to visual category descriptions. A component-based object representation is presented. An instance based approach is used for category representation. An instance is described by its components and geometric relations between them. Each component is a color blob or an aggregation of neighboring color blobs. The categorization strategy is based on graph matching. The learning/grounding capacity of the robot is assessed over a series of semi-automated experiments and the results are reported. © 2009 Springer Berlin Heidelberg.
dc.description5816 LNAI
dc.description
dc.description263
dc.description274
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dc.descriptionKirby, S., Hurford, J., The Emergence of Linguistic Structure: An overview of the Iterated Learning Model (2002) Simulating the Evolution of Language, pp. 121-148. , Cangelosi, A., Parisi D. (eds.) .Springer, Heidelberg
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dc.descriptionSeabra Lopes, L., Connell, J.H., Semisentient robots: Routes to integrated intelligence (2001) IEEE Intelligent Systems, 16 (5), pp. 10-14
dc.descriptionSeabra Lopes, L., Chauhan, A., How many Words can my Robot learn? An Approach and Experiments with One-Class Learning (2007) Interaction Studies, 8 (1), pp. 53-81
dc.descriptionSeabra Lopes, L., Chauhan, A., Silva, J., Towards long-term visual learning of object categories in human-robot interaction (2007) New Trends in Artificial Intelligence, APPIA, pp. 623-634. , Maia Neves, J.C., Santos, M.F., Machado, J.M. (eds.)
dc.descriptionSeabra Lopes, L., Chauhan, A., Open-ended category learning for language acquisition (2008) Connection Science, 8 (4)
dc.descriptionSteels, L., Language games for autonomous robots (2001) IEEE Intelligent Systems, 16 (5), pp. 16-22
dc.descriptionSteels, L., Kaplan, F., AIBO's first words: The social learning of language and meaning (2002) Evolution of Communication, 4 (1), pp. 3-32
dc.descriptionSteels, L., Evolving Grounded Communication for Robots (2003) Trends in Cognitive Science, 7 (7), pp. 308-312
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dc.languageen
dc.publisher
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsfechado
dc.sourceScopus
dc.titleEmbodied Language Acquisition: A Proof Of Concept
dc.typeActas de congresos


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