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      Gaze detection as a social cue to initiate natural human-robot collaboration in an assembly task

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          Abstract

          Introduction: In this work we explore a potential approach to improve human-robot collaboration experience by adapting cobot behavior based on natural cues from the operator.

          Methods: 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 employed a gaze-based attention recognition model to identify when the participants look at the cobot.

          Results: Our results indicate that in most cases (83.74%), 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 mostly around the time of the joint activity. Given the above results, a fully integrated system triggering joint action only when the gaze is directed towards the cobot was piloted with 10 volunteers, of which one characterized by high-functioning Autism Spectrum Disorder. Even though they had never interacted with the robot and did not know about the gaze-based triggering system, most of them successfully collaborated with the cobot and reported a smooth and natural interaction experience.

          Discussion: 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 and to attempt the full integration of an automated gaze-based triggering system.

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          Most cited references37

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          The eye contact effect: mechanisms and development.

          The 'eye contact effect' is the phenomenon that perceived eye contact with another human face modulates certain aspects of the concurrent and/or immediately following cognitive processing. In addition, functional imaging studies in adults have revealed that eye contact can modulate activity in structures in the social brain network, and developmental studies show evidence for preferential orienting towards, and processing of, faces with direct gaze from early in life. We review different theories of the eye contact effect and advance a 'fast-track modulator' model. Specifically, we hypothesize that perceived eye contact is initially detected by a subcortical route, which then modulates the activation of the social brain as it processes the accompanying detailed sensory information.
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            Industry 4.0 and Industry 5.0—Inception, conception and perception

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              Social Eye Gaze in Human-Robot Interaction: A Review

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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2396087/overviewRole: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2060449/overviewRole: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2408804/overviewRole: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2757898/overviewRole: Role:
                URI : https://loop.frontiersin.org/people/1612889/overviewRole: Role: Role:
                URI : https://loop.frontiersin.org/people/361180/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2541390/overviewRole: Role: Role: Role: Role:
                Journal
                Front Robot AI
                Front Robot AI
                Front. Robot. AI
                Frontiers in Robotics and AI
                Frontiers Media S.A.
                2296-9144
                17 July 2024
                2024
                : 11
                : 1394379
                Affiliations
                [1] 1 Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing , National Research Council of Italy , Lecco, Italy
                [2] 2 Industrial Engineering Department , University of Bologna , Bologna, Italy
                [3] 3 Human-Centered Artificial Intelligence , University of Augsburg , Augsburg, Germany
                [4] 4 Catholic University of the Sacred Heart , Psychology Department , Milan, Italy
                Author notes

                Edited by: Ganix Lasa, University of Mondragón, Spain

                Reviewed by: Juan S. Toquica, Federal University of Ceara, Brazil

                Barbara Deml, Karlsruhe Institute of Technology (KIT), Germany

                *Correspondence: Matteo Lavit Nicora, matteo.lavit@ 123456stiima.cnr.it
                Article
                1394379
                10.3389/frobt.2024.1394379
                11288793
                39086514
                bf737279-30e2-4cee-ab30-221fe7e917a6
                Copyright © 2024 Lavit Nicora, Prajod, Mondellini, Tauro, Vertechy, André and Malosio.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 05 March 2024
                : 26 June 2024
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 847926 MindBot.
                Categories
                Robotics and AI
                Original Research
                Custom metadata
                Industrial Robotics and Automation

                human-robot interaction,industry 5.0,gaze estimation,natural behavior,human-centered computing

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