Eyes on you: field study of robot vendor using humanlike eye component xe2x80x9cAkagachixe2x80x9d Eye gaze is an important nonverbal behavior for communication robots as it serves as the onset of communication. Existing communication robots have various eyes because design choices for an appropriate eye have yet to be determined, so many robots are designed on the basis of individual designersxe2x80x99 ideas. Thus, this study focuses on humanlike eye gaze in a real environment. We developed an independent humanlike eye gaze component called Akagachi for various robots and conducted an observational field study by implementing it to a vendor robot called Reika. We conducted a field study in a theme park where Reika sells softserve ice cream in a food stall and analyzed the behaviors of 984 visitors. Our results indicate that Reika elicits significantly more interaction from people with eye gaze than without it.

Measuring engagement elicited by eye contact in HumanRobot Interaction The present study aims at investigating how eye contact established by a humanoid robot affects engagement in humanrobot interaction (HRI). To this end, we combined explicit subjective evaluations with implicit measures, i.e. reaction times and eye tracking. More specifically, we employed a gaze cueing paradigm in HRI protocol involving the iCub robot. Critically, before moving its gaze, iCub either established eye contact or not with the user. We investigated the patterns of fixations of participantsxe2x80x99 gaze on the robotxe2x80x99s face, joint attention and the subjective ratings of engagement as a function of eye contact or no eye contact. We found that eye contact affected implicit measures of engagement, i.e. longer fixation times on the robotxe2x80x99s face during eye contact. Moreover, we showed that joint attention was elicited only when the robot established eye contact, whereas no joint attention occurred when it did not. On the contrary, explicit measures of engagement with the robot did not vary across conditions. Our results highlight the value of combining explicit with implicit measures in an HRI protocol in order to unveil underlying human cognitive mechanisms, which might be at stake during the interactions. These mechanisms could be crucial for establishing an effective and engaging HRI, and provide guidelines to the robotics community with respect to better robot design.