Endowing a NAO Robot With Practical Social-Touch Perception
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Endowing a NAO Robot With Practical Social-Touch Perception. / Burns, Rachael Bevill; Lee, Hyosang; Seifi, Hasti; Faulkner, Robert; Kuchenbecker, Katherine J.
In: Frontiers in Robotics and AI, Vol. 9, 840335, 2022, p. 1-17.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Endowing a NAO Robot With Practical Social-Touch Perception
AU - Burns, Rachael Bevill
AU - Lee, Hyosang
AU - Seifi, Hasti
AU - Faulkner, Robert
AU - Kuchenbecker, Katherine J.
N1 - Publisher Copyright: Copyright © 2022 Burns, Lee, Seifi, Faulkner and Kuchenbecker.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - affective touch
KW - gesture classification
KW - human-robot interaction
KW - social touch
KW - socially assistive robotics
KW - tactile sensors
UR - http://www.scopus.com/inward/record.url?scp=85129560769&partnerID=8YFLogxK
U2 - 10.3389/frobt.2022.840335
DO - 10.3389/frobt.2022.840335
M3 - Journal article
C2 - 35516789
AN - SCOPUS:85129560769
VL - 9
SP - 1
EP - 17
JO - Frontiers in Robotics and AI
JF - Frontiers in Robotics and AI
SN - 2296-9144
M1 - 840335
ER -
ID: 307373493