Gaze Detection and Analysis for Initiating Joint Activity in Industrial Human-Robot Collaboration

• URL: http://arxiv.org/abs/2312.06643v3
• Date: Thu, 1 Feb 2024 18:05:36 GMT
• Title: Gaze Detection and Analysis for Initiating Joint Activity in Industrial Human-Robot Collaboration
• Authors: Pooja Prajod, Matteo Lavit Nicora, Marta Mondellini, Giovanni Tauro, Rocco Vertechy, Matteo Malosio, Elisabeth Andr\'e
• Abstract summary: A potential approach to improve the collaboration experience involves adapting cobot behavior based on natural cues from the operator. Inspired by the literature on human-human interactions, we conducted a wizard-of-oz study to examine whether a gaze towards the cobot can serve as a trigger for initiating joint activities.
• Score: 3.775062086401102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative robots (cobots) are widely used in industrial applications, yet extensive research is still needed to enhance human-robot collaborations and operator experience. A potential approach to improve the collaboration experience involves adapting cobot behavior based on natural cues from the operator. Inspired by the literature on human-human interactions, we conducted a wizard-of-oz study to examine whether a gaze towards the cobot can serve as a trigger for initiating joint activities in collaborative sessions. In this study, 37 participants engaged in an assembly task while their gaze behavior was analyzed. We employ a gaze-based attention recognition model to identify when the participants look at the cobot. Our results indicate that in most cases (84.88\%), the joint activity is preceded by a gaze towards the cobot. Furthermore, during the entire assembly cycle, the participants tend to look at the cobot around the time of the joint activity. To the best of our knowledge, this is the first study to analyze the natural gaze behavior of participants working on a joint activity with a robot during a collaborative assembly task.

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