  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 generated by 24 participants driving a simulated 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 thus 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.
