Accurately measuring perceptions of robots has become increasingly important as technological progress permits more frequent and extensive interaction between people and robots. Across four studies, we develop and validate a scale to measure social perception of robots. Drawing from the Godspeed Scale and from the psychological literature on social perception, we develop an 18-item scale (The Robotic Social Attribute Scale; RoSAS) to measure people's judgments of the social attributes of robots. Factor analyses reveal three underlying scale dimensions-warmth, competence, and discomfort. We then validate the RoSAS and show that the discomfort dimension does not reflect a concern with unfamiliarity. Using images of robots that systematically vary in their machineness and gender-typicality, we show that the application of these social attributes to robots varies based on their appearance.

Recent research indicates that robots' physical appearance affects inferences about their social traits, but little research has examined how features may influence evaluative responses (i.e., liking of and willingness to interact). The current studies investigated how robot appearance and function affect evaluative responses. Across four experiments, we assessed how robotic facial features (Study 1) and roles (e.g., companion vs. military; Study 2) influence trait inferences and evaluative responses, and the relationship between them. Study 3 examined the association between robot facial features and roles, and Study 4 examined both facial features and roles to see which drove social judgments. Results indicated that trait inferences and evaluative responses vary in response to both facial features and roles. Trait inferences predicted, with differing strength, evaluative responses towards robots. When pitting facial features against roles, features accounted more strongly for judgments. Implications for human-robot interaction and robot design are considered.