Steven A. Vasquez

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.

A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work.