Humans and industrial collaborative robots (cobots) form intricate relationships as they both possess agency, share spaces, and must coordinate effectively to work together. In initial interactions, much like human-team dynamics, the perceived contribution of each agent plays a crucial role in shaping the collaborative experience and the acceptance of cobots. Beyond unilateral support perceptions from or to the cobot—termed as Received Support (RS) and Given Support (GS)—Mutual Support (MS) embodies a balanced, reciprocal dynamic where humans view their contribution as equal to that of the cobot. Drawing from social knowledge in human teams and aligning with recent interpretations of human-robot interactions, we propose that perceiving MS can foster fluent, trusting, and harmonious collaboration, ultimately increasing cobot acceptance in work routines. In this observational study, 56 novices collaborated with the industrial cobot UR10e to assess support dynamics. We explored how perceptions of balanced (MS) or unbalanced support (RS and GS) impact task performance (i.e., task time), Cognitive Factors (COG-F), User Experience (UX-F), and Human-Robot Relationship Factors (HRR-F), ultimately influencing cobot acceptance. We also examined the influence of initial attitudes toward robots. The findings suggest that fostering perceptions of MS, rather than focusing solely on functionality or work relief (RS), can lead to smoother integration and greater acceptance of cobots, especially among novices, regardless of their initial attitudes toward robots.

The increasingly aging workforce is bringing particular attention to senior individuals in production sectors. While the interest in Virtual Reality (VR) applications for industrial robotics grows, the question of whether and how senior workers can withstand VR-based repetitive tasks arises. We here aimed to answer such questions by systematically assessing young and senior users’ experiential, behavioural, and cognitive factors during simulated robotic teleoperations in VR. Two control systems for VR telerobotics, button- and action-based controls, were employed. Human performance, vigilance, and workload were measured through self-reports and a VR-integrated eye-tracker. Additionally, age-dependent differences in individual cultural and experiential factors were explored via self-report measures. Despite being slower and experiencing increased fatigue under specific conditions, as suggested by the eye-tracking measures, senior users demonstrated comparable precision in operating the robotic arm to their younger counterparts. Notably, both age groups reported similar levels of perceived fatigue. The paper provides an in-depth analysis of the advantages and challenges of adopting advanced telerobotics control systems across different age groups, consistently emphasising the human-centered dimension.

Virtual Reality (VR) is gaining ground in the robotics and teleoperation industry, opening new prospects as a novel computerized methodology to make humans interact with robots. In contrast with more conventional button-based teleoperations, VR allows users to use their physical movements to drive robotic systems in the virtual environment. The latest VR devices are also equipped with integrated eye-tracking, which constitutes an exceptional opportunity for monitoring users’ workload online. However, such devices are fairly recent, and human factors have been consistently marginalized so far in telerobotics research. We thus covered these aspects by analyzing extensive behavioral data during simulated guidance of an industrial robot in VR through a pick-and-place task. Users drove the robot via button-based and action-based controls and under low (single-task) and high (dual-task) mental demands. We collected self-reports, performance and eye-tracking data. Specifically, we asked i) how the interactive features of VR affect users’ performance and workload, and additionally tested ii) the sensibility of diverse eye parameters in monitoring users’ vigilance and workload throughout the task. Users performed faster and more accurately, while also showing a lower mental workload, when using an action-based VR control. Among the eye parameters, pupil size was the most resilient indicator of workload, as it was highly correlated with the self-reports and was not affected by the user's degree of physical motion in VR. Our results bring a fresh human-centric overview of human-robot interactions in VR, and systematically demonstrate the potential of VR devices for monitoring human factors in telerobotics contexts.

A valid Human-Robot Interaction (HRI) should be effective for the majority of the population. However, gender, gaming experience, or other individual factors are often likely to affect users' performance when interacting with a robot. In the present study, we measured the performance and perceived workload of participants driving a robot through a pick-and-place task in Virtual Reality (VR) via controller buttons or physical actions. The following individual factors were considered in the analysis: gaming experience, gender, learnability skills, problem solving and trust in technology. Results showed that all the accounted individual factors impacted either performance or perceived demand, but only when guiding the robot via controller buttons. Our findings foster the adoption of more natural ways of teleoperating robots, such as by physical actions, as they demonstrated to be exempt from the influence of individual factors, and are likely to be effective for a broader section of the population.