Conference paper Open Access

Eager to Learn vs. Quick to Complain? How a socially adaptive robot architecture performs with different robot personalities

Tanevska, Ana; Rea, Francesco; Sandini, Giulio; Canamero, Lola; Sciutti, Alessandra


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  <identifier identifierType="URL">https://zenodo.org/record/3931277</identifier>
  <creators>
    <creator>
      <creatorName>Tanevska, Ana</creatorName>
      <givenName>Ana</givenName>
      <familyName>Tanevska</familyName>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
    <creator>
      <creatorName>Rea, Francesco</creatorName>
      <givenName>Francesco</givenName>
      <familyName>Rea</familyName>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
    <creator>
      <creatorName>Sandini, Giulio</creatorName>
      <givenName>Giulio</givenName>
      <familyName>Sandini</familyName>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
    <creator>
      <creatorName>Canamero, Lola</creatorName>
      <givenName>Lola</givenName>
      <familyName>Canamero</familyName>
      <affiliation>University of Hertfordshire Hatfield, Dept. of Computer Science</affiliation>
    </creator>
    <creator>
      <creatorName>Sciutti, Alessandra</creatorName>
      <givenName>Alessandra</givenName>
      <familyName>Sciutti</familyName>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Eager to Learn vs. Quick to Complain? How a socially adaptive robot architecture performs with different robot personalities</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Social robots and social learning</subject>
    <subject>Human-human and human-robot interaction and communication</subject>
    <subject>Architectures for Cognitive Development and Open-Ended Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-11-28</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3931277</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/SMC.2019.8913903</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/contact_unit_iit</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;A social robot that is aware of our needs and continuously adapts its behaviour to them has the potential of creating a complex, personalized, human-like interaction of the kind we are used to have with our peers in our everyday lives. We are interested in exploring how would an adaptive architecture function and personalize to different users when given different initial values of its variables, i.e. when implementing the same adaptive framework with different robot personalities. Would an architecture that learns very quickly outperform a slower but steadier learning profile? To further explore this, we propose a cognitive architecture for the humanoid robot iCub supporting adaptability and we attempt to validate its functionality and test different robot profiles.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/804388/">804388</awardNumber>
      <awardTitle>investigating Human Shared PErception with Robots</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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