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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  <dc:creator>Jonas Gonzalez-BIllandon</dc:creator>
  <dc:creator>Lukas Grasse</dc:creator>
  <dc:creator>Alessandra Sciutti</dc:creator>
  <dc:creator>Matthew Tata</dc:creator>
  <dc:creator>Francesco Rea</dc:creator>
  <dc:date>2019-11-08</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/3936610</dc:identifier>
  <dc:identifier>10.5281/zenodo.3936610</dc:identifier>
  <dc:identifier>oai:zenodo.org:3936610</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.3936609</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Self supervised learning</dc:subject>
  <dc:subject>Deep Learning</dc:subject>
  <dc:subject>Cognitive development</dc:subject>
  <dc:title>Cognitive Architecture for Joint Attentional Learning of word-object mapping with a Humanoid Robot</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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