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Using Robot Adaptivity to Support Learning in Child-Robot Interaction

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Social Robotics (ICSR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12483))

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Abstract

Previous research has shown that if a robot invests physical effort in teaching human partners a new skill, the teaching will be more effective and the partners will reciprocate by investing more effort and patience when their turn to teach comes. In the current study, we extend this research to child-robot interaction. To this end, we devised a scenario in which a humanoid robot (iCub) and a child participant alternated in teaching each other new skills. In the robot teaching phase iCub taught participants sequences of movements, which they had to memorize and repeat. The robot then repeated the demonstration a second time: in the high effort (or Adaptive) condition, the iCub slowed down its movements when repeating the demonstration whereas in the low effort (or Unadaptive) condition he sped the movements up. In the participant teaching phase, children were asked to give the robot a demonstration of three symbols, and then to repeat it if the robot had not understood.

The results reveal that children learned the sequences more effectively when the iCub adapted its movements to the learner, and that, when their turn to teach to the robot came, they slowed down and increased segmentation when repeating the demonstration.

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Acknowledgment

The research has been supported by a Starting Grant from the European Research Council (nr. 679092, SENSE OF COMMITMENT).

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Authors and Affiliations

  1. COgNiTive Architecture for Collaborative Technologies Unit, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152, Genoa, Italy

    Alessia Vignolo & Alessandra Sciutti

  2. Department of Philosophy, Social Sciences Building, University of Warwick, Coventry, CV4 7AL, UK

    John Michael

Authors
  1. Alessia Vignolo
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  2. Alessandra Sciutti
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  3. John Michael
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Corresponding author

Correspondence to Alessia Vignolo .

Editor information

Editors and Affiliations

  1. Pennsylvania State University, University Park, PA, USA

    Alan R. Wagner

  2. University of Nevada Reno, Reno, NV, USA

    David Feil-Seifer

  3. University of Denver, Denver, CO, USA

    Kerstin S. Haring

  4. University of Naples Federico II, Naples, Italy

    Silvia Rossi

  5. Colorado School of Mines, Golden, CO, USA

    Thomas Williams

  6. Wichita State University, Wichita, KS, USA

    Hongsheng He

  7. National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

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Vignolo, A., Sciutti, A., Michael, J. (2020). Using Robot Adaptivity to Support Learning in Child-Robot Interaction. In: Wagner, A.R., et al. Social Robotics. ICSR 2020. Lecture Notes in Computer Science(), vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_36

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  • DOI: https://doi.org/10.1007/978-3-030-62056-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62055-4

  • Online ISBN: 978-3-030-62056-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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