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Over the past years, extensive research has been dedicated to developingrobust platforms and data-driven dialog models to support long-term human-robotinteractions. However, little is known about how people's perception of robotsand engagement with them develop over time and how these can be accuratelyassessed through implicit and continuous measurement techniques. In this paper,we explore this by involving participants in three interaction sessions withmultiple days of zero exposure in between. Each session consists of a jointtask with a robot as well as two short social chats with it before and afterthe task. We measure participants' gaze patterns with a wearable eye-trackerand gauge their perception of the robot and engagement with it and the jointtask using questionnaires. Results disclose that aversion of gaze in a socialchat is an indicator of a robot's uncanniness and that the more people gaze atthe robot in a joint task, the worse they perform. In contrast with most HRIliterature, our results show that gaze towards an object of shared attention,rather than gaze towards a robotic partner, is the most meaningful predictor ofengagement in a joint task. Furthermore, the analyses of gaze patterns inrepeated interactions disclose that people's mutual gaze in a social chatdevelops congruently with their perceptions of the robot over time. These arekey findings for the HRI community as they entail that gaze behavior can beused as an implicit measure of people's perception of robots in a social chatand of their engagement and task performance in a joint task.

I Can See it in Your Eyes: Gaze as an Implicit Cue of Uncanniness and Task Performance in Repeated Interactions