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Title: Theoretical Considerations for Social Learning between a Human Observer and a Robot Model
Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.
Authors:
;
Award ID(s):
1909847
Publication Date:
NSF-PAR ID:
10288682
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
ISSN:
1071-1813
Sponsoring Org:
National Science Foundation
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