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

Authors :
Matteo Lavit Nicora
Pooja Prajod
Marta Mondellini
Giovanni Tauro
Rocco Vertechy
Elisabeth André
Matteo Malosio
Source :
Frontiers in Robotics and AI, Vol 11 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

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.

Details

Language :
English
ISSN :
22969144
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Robotics and AI
Publication Type :
Academic Journal
Accession number :
edsdoj.f145a86c0f5e47f0b5f7337a84ada0db
Document Type :
article
Full Text :
https://doi.org/10.3389/frobt.2024.1394379