  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.
