Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-04

AUTHORS

Sofiane Boucenna, David Cohen, Andrew N Meltzoff, Philippe Gaussier, Mohamed Chetouani

ABSTRACT

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture--specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot's motor internal state, (iii) posture recognition, and (iv) novelty detection--is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning. More... »

PAGES

19908

References to SciGraph publications

  • 2015-02. Towards Artificial Empathy in INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
  • 2014-12. Interactive Technologies for Autistic Children: A Review in COGNITIVE COMPUTATION
  • 2014-11. A Robot Learns the Facial Expressions Recognition and Face/Non-face Discrimination Through an Imitation Game in INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
  • 2012-08. Measuring Human-Robot Interaction Through Motor Resonance in INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/srep19908

    DOI

    http://dx.doi.org/10.1038/srep19908

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1023441643

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26844862


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    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/srep19908'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/srep19908'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/srep19908'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/srep19908'


     

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