Conference paper Open Access

Magic iCub: a Humanoid Robot Autonomously Catching Your Lies in a Card Game

Dario Pasquali; Jonas Gonzalez-Billandon; Francesco Rea; Giulio Sandini; Alessandra Sciutti


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  <identifier identifierType="URL">https://zenodo.org/record/4405851</identifier>
  <creators>
    <creator>
      <creatorName>Dario Pasquali</creatorName>
      <affiliation>Robotics, Brain and Cognitive Science (RBCS), Istituto Italiano di Tecnologia (IIT), DIBRIS</affiliation>
    </creator>
    <creator>
      <creatorName>Jonas Gonzalez-Billandon</creatorName>
      <affiliation>Robotics, Brain and Cognitive Science (RBCS), Istituto Italiano di Tecnologia (IIT), DIBRIS</affiliation>
    </creator>
    <creator>
      <creatorName>Francesco Rea</creatorName>
      <affiliation>Robotics, Brain and Cognitive Science (RBCS), Istituto Italiano di Tecnologia (IIT)</affiliation>
    </creator>
    <creator>
      <creatorName>Giulio Sandini</creatorName>
      <affiliation>Robotics, Brain and Cognitive Science (RBCS), Istituto Italiano di Tecnologia (IIT)</affiliation>
    </creator>
    <creator>
      <creatorName>Alessandra Sciutti</creatorName>
      <affiliation>COgNiTive Architecture for Collaborative Technologies (CONTACT), Istituto Italiano di Tecnologia (IIT)</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Magic iCub: a Humanoid Robot Autonomously Catching Your Lies in a Card Game</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Entertainment, magic, human-robot interaction, pupillometry, cognitive load</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-03-08</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4405851</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3434073.3444682</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;Games are often used to foster human partners&amp;rsquo; engagement and natural behavior, even when they are played with or against robots. Therefore, beyond their entertainment value, games represent ideal interaction paradigms where to investigate natural human-robot interaction and to foster robots&amp;rsquo; diffusion in the society. However, most of the state-of-the-art games involving robots, are driven with a Wizard of Oz approach. To address this limitation, we present an end-to-end (E2E) architecture to enable the iCub robotic platform to autonomously lead an entertaining magic card trick with human partners. We demonstrate that with this architecture a robot is capable of autonomously directing the game from beginning to end. In particular, the robot could detect in real-time when the players lied in the description of one card in their hands (the &lt;em&gt;secret card).&lt;/em&gt; In a validation experiment, the robot achieved an accuracy of 88.2% (against a chance level of 16.6%) in detecting the &lt;em&gt;secret card&lt;/em&gt; while the social interaction naturally unfolded. The results demonstrate the feasibility of our approach and its effectiveness in maintaining engagement of the players and entertaining the participants. Additionally, we provide evidence on the possibility to detect important measures of the human partner`s inner state such as cognitive load related to lie creation with pupillometry in a short and ecological game-like interaction with a robot.&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>
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