{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24151"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24151","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Autonomous learning of action-word semantics in a humanoid robot","abstract":"For creation of an artificial agent that is capable of using language naturally, models that only manipulate symbols or classify speech are ineffective. The semantic information which language conveys must be grounded in the agent’s complete sensorimotor experience. Typically, patterns from visual, auditory, and proprioceptive data streams which share the same conceptual cause are fused together in an associative memory at the core of the language model. Coupling of motor and auditory modalities, which is crucial for a large part of semantic understanding, presents a particularly difficult challenge. Words and actions both need models capable of capturing spatial and temporal structure, and training algorithms that can learn in a self-organizing, incremental fashion. Presented is a method for online learning of word and action lexicons based on the hidden Markov model. The model is then evaluated through action-word learning experiments implemented on a humanoid robot.","abstract_html":"For creation of an artificial agent that is capable of using language naturally, models that only manipulate symbols or classify speech are ineffective. The semantic information which language conveys must be grounded in the agent’s complete sensorimotor experience. Typically, patterns from visual, auditory, and proprioceptive data streams which share the same conceptual cause are fused together in an associative memory at the core of the language model. Coupling of motor and auditory modalities, which is crucial for a large part of semantic understanding, presents a particularly difficult challenge. Words and actions both need models capable of capturing spatial and temporal structure, and training algorithms that can learn in a self-organizing, incremental fashion. Presented is a method for online learning of word and action lexicons based on the hidden Markov model. The model is then evaluated through action-word learning experiments implemented on a humanoid robot.","abstract_has_math":false,"creators":["Niehaus, Logan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Levinson, Stephen E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T14:57:30Z","date_published":"2011-05-25T14:57:30Z","updated_at":"2026-07-22T22:25:23Z","subjects":["language acquisition","autonomous mental development","cognitive robotics","embodied cognition"],"languages":["en"],"rights":["Copyright 2011 Logan Niehaus"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24151","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Levinson, Stephen E."]},{"key":"dc:creator","label":"Author","values":["Niehaus, Logan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T14:57:30Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["language acquisition","autonomous mental development","cognitive robotics","embodied cognition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2011 Logan Niehaus"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24151"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["For creation of an artificial agent that is capable of using language naturally, models that only manipulate symbols or classify speech are ineffective. 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Coupling of motor and auditory modalities, which is crucial for a large part of semantic understanding, presents a particularly difficult challenge. Words and actions both need models capable of capturing spatial and temporal structure, and training algorithms that can learn in a self-organizing, incremental fashion. Presented is a method for online learning of word and action lexicons based on the hidden Markov model. The model is then evaluated through action-word learning experiments implemented on a humanoid robot.","Item withdrawn by Alexis Thompson (athmpsn1@illinois.edu) on 2011-04-20T16:43:06Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Niehaus_Logan.pdf: 524344 bytes, checksum: 9adf093333813feeec5af3a3e62d6825 (MD5)","Made available in DSpace on 2011-05-25T14:57:30Z (GMT). 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