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More than just co-workers: Presence of humanoid robot co-worker influences human performance

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  • Ashesh Vasalya
  • Gowrishankar Ganesh
  • Abderrahmane Kheddar

Abstract

Does the presence of a robot co-worker influence the performance of humans around it? Studies of motor contagions during human-robot interactions have examined either how the observation of a robot affects a human’s movement velocity, or how it affects the human’s movement variance, but never both together. Performance however, has to be measured considering both task speed (or frequency) as well as task accuracy. Here we examine an empirical repetitive industrial task in which a human participant and a humanoid robot work near each other. We systematically varied the robot behavior, and observed whether and how the performance of a human participant is affected by the presence of the robot. To investigate the effect of physical form, we added conditions where the robot co-worker torso and head were covered, and only the moving arm was visible to the human participants. Finally, we compared these behaviors with a human co-worker, and examined how the observed behavioral affects scale with experience of robots. Our results show that human task frequency, but not task accuracy, is affected by the observation of a humanoid robot co-worker, provided the robot’s head and torso are visible.

Suggested Citation

  • Ashesh Vasalya & Gowrishankar Ganesh & Abderrahmane Kheddar, 2018. "More than just co-workers: Presence of humanoid robot co-worker influences human performance," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
  • Handle: RePEc:plo:pone00:0206698
    DOI: 10.1371/journal.pone.0206698
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    References listed on IDEAS

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    1. Atsushi Takagi & Gowrishankar Ganesh & Toshinori Yoshioka & Mitsuo Kawato & Etienne Burdet, 2017. "Physically interacting individuals estimate the partner’s goal to enhance their movements," Nature Human Behaviour, Nature, vol. 1(3), pages 1-6, March.
    2. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    3. Etienne Burdet & Rieko Osu & David W. Franklin & Theodore E. Milner & Mitsuo Kawato, 2001. "The central nervous system stabilizes unstable dynamics by learning optimal impedance," Nature, Nature, vol. 414(6862), pages 446-449, November.
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    Cited by:

    1. Małgorzata Dobrowolska & Lilla Knop, 2020. "Fit to Work in the Business Models of the Industry 4.0 Age," Sustainability, MDPI, vol. 12(12), pages 1-18, June.

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