The Influence of Gaming Experience, Gender and Other Individual Factors on Robot Teleoperations in VR

Abstract

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.

You Can’t Hide Behind Your Headset: User Profiling in Augmented and Virtual Reality

“…Our previous studies assessed behavioral and workload aspects in individuals using AR while walking outdoor [11], and in users wearing VR for guiding an industrial robotic arm [12], [60]. In the present work, we leverage behavioral and eye-tracking data of both the AR and VR scenarios with the purpose of profiling users.…”

“…In the dual-task, participants also performed simple arithmetic sums, typing the results on a virtual keyboard by pointing the controller. Further details about the task design can be found in [60]. In each condition, the young participants performed 40 trials, while the old participants performed 20 trials.…”

“…These results align with recent literature on behavioral gender differences in the VR pick-and-place task. For instance, our previous work [60] demonstrated how men outperformed women in the pick-andplace tasks in terms of task execution time, particularly when using controller buttons. These differences might have been even more marked when performing an additional mental task, thus allowing more precise gender profiling.…”

What you see is (not) what you get: A VR Framework for Correcting Robot Errors

“…Several recent projects have focused on improving human-robot collaboration using virtual, augmented and mixed reality (VAM) devices. Teleoperation (remotely controlling the robot) is one of the most common forms of human-robot interaction [17,18,22]. Ostatin [20] and Togias [24] both use VR to plan the robotic arm's trajectory.…”