First-Hand Impressions: Charting and Predicting User Impressions of Robot Hands

Abstract

Designing robotic hands has been an active area of research and innovation in the last decade. However, little is known about how people perceive robot hands and react to being touched by them. To inform hand design for social robots, we created a database of 73 robot hands and ran two user studies. In the first study, 160 online users rated the hands in our database. Variations in user ratings mostly centered on the perceived Comfortableness , Interestingness , and Industrialness of the hands. In a second lab-based study, users evaluated seven physical hands and had similar ratings to results from the online study. Furthermore, we did not find a significant difference in user ratings before and after the users were touched by the hands. We provide regression models that can predict user ratings from the hand features (e.g., number of fingers) and an online interface for using our robot hand database and predictive models.