Conference paper Open Access

Cognitive Architecture for Joint Attentional Learning of word-object mapping with a Humanoid Robot

Jonas Gonzalez-BIllandon; Lukas Grasse; Alessandra Sciutti; Matthew Tata; Francesco Rea


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  <identifier identifierType="DOI">10.5281/zenodo.3936610</identifier>
  <creators>
    <creator>
      <creatorName>Jonas Gonzalez-BIllandon</creatorName>
      <affiliation>Italian Institute of technology, University of Genova</affiliation>
    </creator>
    <creator>
      <creatorName>Lukas Grasse</creatorName>
      <affiliation>University of Lethbridge</affiliation>
    </creator>
    <creator>
      <creatorName>Alessandra Sciutti</creatorName>
      <affiliation>Italian Institute of technology</affiliation>
    </creator>
    <creator>
      <creatorName>Matthew Tata</creatorName>
      <affiliation>University of Lethbridge</affiliation>
    </creator>
    <creator>
      <creatorName>Francesco Rea</creatorName>
      <affiliation>Italian Institute of technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Cognitive Architecture for Joint Attentional Learning of word-object mapping with a Humanoid Robot</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Self supervised learning</subject>
    <subject>Deep Learning</subject>
    <subject>Cognitive development</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-11-08</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3936610</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3936609</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Since infancy humans can learn from social context to associate words with their meanings, for example associating names with objects. The open-question is which computational framework could replicate the abilities of toddlers in developing language and its meaning in robots. We propose a computational framework in this paper to be implemented on a robotics platform to replicate the early learning process of humans for the specific task of word-object mapping.&lt;/p&gt;</description>
  </descriptions>
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