Personality of social robots perceived through the appearance.

Past research showed that people are able to perceive the personality of others at zero acquaintances. There are two main ways, verbal and non-verbal methods, which play an important role for one in perceiving personality of others. Extensive research was conducted in relating personality with verbal, paralinguistic and gestures cues. However, there are not much research, to our knowledge, that relates the appearance and perceived personality of robots. The main objective of this research is to relate individual design features with big five perceived personality of the robots. We used the results of rated perceptions across 100 pictorial images of robots and relate the results with the 40 individual design features using General Linear Model (GLM). The initial results of the GLM analysis showed that participants' rating of personality of robot fell along the dimension of perceived friendliness which is a common rotation of extroversion and agreeableness. Some relationships were found between humanlike design features and perceived friendliness of robots. Since participants are more familiar with humans, participants perceived robots with humanlike features friendlier than the others. Some other findings such as color and surface material were found related with participants' perceived friendliness as well. In the future, we will work on the analysis of the main and interaction effects of individual features on user's perceived friendliness.