Designing for gender, age & proficiency: an empirical study of the cognitive ergonomics of dual-task interference in industrial human-robot collaboration

Abstract

Introduction This article reports an empirical study on Human–Robot Collaboration (HRC) under Dual-Task Interference (DTI). To the best of our knowledge, prior research has not investigated the intersection of Dual-Task Interference (DTI) and Human–Robot Collaboration (HRC) in industrial contexts. This study represents one of the first empirical attempts to understand DTI-induced human limitations in collaborative robotics, particularly in industrial HRC settings where stochastic interruptions and competing cognitive demands are common phenomena. Methods The DTI phenomenon was implemented in a cognitive joint-assembly task involving 172 participants. Each participant collaborated with two robots while simultaneously performing a cognitive task designed to induce DTI during team-work. The performance measures included the number of Human–Robot interactions (measuring user engagement), total robot idle time (measuring team fluency), and the total number of completed cognitive tasks (measuring task performance). Results Females exhibited significantly lower robot idle times (12%) and higher Human–Robot interactions (14%), indicating greater collaboration with the robot and more fluent team collaboration than males. Adolescents completed the cognitive task significantly faster than adults (21%) and experts (22%), whereas adults and experts demonstrated greater fluency in team collaboration. No significant differences were observed between novices and experts. Novices successfully bridged the skill gap by distributing their attention equivalently across all aspects of the team-work, resulting in performance comparable to that of experts. Discussion The results reveal that (a) gender and age-group differences significantly influence HRC in DTI-based tasks, (b) performance speed in the primary task negatively affects the team fluency of HRC, and (c) effective team coordination skills are equally as important as task proficiency in HRC. Conclusion This study contributes to design practice by demonstrating that collaborative robot learning algorithms can classify users into specific population clusters based on their characteristic patterns of collaboration with robots for improved performance and user experience (UX). Furthermore, task proficiency and team coordination emerge as two distinct and mutually independent skills in DTI-based tasks.