Endowing a NAO Robot With Practical Social-Touch Perception

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  • Rachael Bevill Burns
  • Hyosang Lee
  • Seifi, Hasti
  • Robert Faulkner
  • Katherine J. Kuchenbecker

Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.

Original languageEnglish
Article number840335
JournalFrontiers in Robotics and AI
Pages (from-to)1-17
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Burns, Lee, Seifi, Faulkner and Kuchenbecker.

    Research areas

  • affective touch, gesture classification, human-robot interaction, social touch, socially assistive robotics, tactile sensors

ID: 307373493