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The aesthetics of a socially assistive robot play a key role in its assistive effectiveness (Tapus, Mataric, and Scassellati 2007). This is because people will often attribute intentions, goals, emotions, and personalities to simple machines with life-like movements or form, which in turn can affect how humans interact with such robotic systems (Reeves and Nass 1996). Studies have already demonstrated that modifying the aesthetics (e.g., human-likeness and face shape) of a socially assistive robot can affect users’ desire to interact with the robot (DiSalvo et al. 2002; Goetz, Kiesler, and Powers 2003; Robins, Dautenhahn, and Dubowski 2006; Zhang et al. 2010).

Different robot appearances were investigated to study what types of appearances encouraged interactions between a child with autism and a robot (Robins, Dautenhahn, and Dubowski 2006). Children with autism were provided with an opportunity to interact with the following: (1) a 45-cm tall doll with “pretty” girls’ clothing and a detailed human-like face; (2) the same doll with a plain and robotic appearance; (3) a life-size plain and featureless “robot” that was a mime artist behaving as a robot; and (4) a robot with human-like appearance (i.e., the mime artist himself). The children’s interactions with the robots were coded for eye gaze (directed at the robot), touch (touching the robot), imitation behavior (imitation of robot movements), and proximity (approaching the robot and staying close to the robot). Results of the study showed that the children gazed, touched, and moved closer to the plain and featureless life-size robot than the life-size human-like robot. They also socially interacted more with the plain-looking life-size robot and ignored the life-size human-like robot. Similar findings occurred with the small humanoid doll: children showed preference for interaction with the robot with a plain and robotic appearance over the doll with pretty clothing.

A study with older adult participants from a retirement home was conducted with a PeopleBot robot to investigate the participants’ perceptions and attitudes regarding the facial features of a medicine delivery robot (Zhang et al. 2010). The robot had one of three different anthropomorphic features when delivering medicine to the participants: a simple face mask with cameras for eyes (Figure 2.4), voice capabilities via a speech synthesizer, or a touch display for user interactivity. Participant perceptions of the robot features were measured using a customized perceived anthropomorphism questionnaire, and participant emotional

Human-like face configuration for the PeopleBot robot with a simple face mask and cameras for eyes

Figure 2.4 Human-like face configuration for the PeopleBot robot with a simple face mask and cameras for eyes. (From Zhang, T., Kaber, D. B., Zhu, B., Swangnetr, M., Mosaly, P., and Hodge, L. 2010. Service robot feature design effects on user perceptions and emotional responses. Intelligent Service Robotics, 3(2): 73-88. © Springer.) responses were measured using the SAM questionnaire. Results of the study showed that human-like features such as a face and voice promoted positive emotional responses based on the SAM questionnaire. Furthermore, when features became more human-like, user perceptions of anthropomorphism increased based on the perceived anthropomorphism questionnaire.

Facial features of the Pearl robot that contributed to perceptions of human-likeness were investigated (DiSalvo et al. 2002). Participants were presented with 48 images of different robot heads and were asked to rate each head on a scale from 1 (not very human-like) to 5 (very human-like). Each robot head was categorized based on the presence of eyes, ears, nose, mouth, eyelids, eyebrows, height/width ratio of the face, percentage of various facial regions (forehead, chin, and other facial features) size of the eyes, distance between the eyes, and width of the mouth. Statistical analysis was then performed to identify the relationship between participant perceptions of robot human-likeness and the robot facial features. Results showed that the presence of eyelids, nose, and mouth increased the perceptions of human-likeness the most. The total number of facial features also increased perceptions of human-likeness. Furthermore, the larger the ratio between the width and height of the robot head, the less human-like it was perceived.

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