The virtualization of human–robot interactions: a user-centric workload assessment

Abstract

Interest in the virtualization of human–robot interactions is increasing, yet the impact that collaborating with either virtual or physical robots has on the human operator’s mental state is still insufficiently studied. In the present work, we aimed to fill this gap by conducting a systematic assessment of a human–robot collaborative framework from a user-centric perspective. Mental workload was measured in participants working in synergistic co-operation with a physical and a virtual collaborative robot (cobot) under different levels of task demands. Performance and implicit and explicit workload were assessed as a function of pupil size variation and self-reporting questionnaires. In the face of a similar self-reported mental demand when maneuvering the virtual or physical cobot, operators showed shorter operation times and lower implicit workload when interacting with the virtual cobot compared to its physical counterpart. Furthermore, the benefits of collaborating with a virtual cobot most vividly manifested when the user had to position the robotic arm with higher precision. These results shed light on the feasibility and importance of relying on multidimensional assessments in real-life work settings, including implicit workload predictors such as pupillometric measures. From a broader perspective, our findings suggest that virtual simulations have the potential to bring significant advantages for both the user's mental well-being and industrial production, particularly for highly complex and demanding tasks.

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

“…Participants physically touched the virtual robot and moved their arms to relocate it over the worktable. In line with our previous findings [12], the actions performed concurrently with the arithmetic task were categorized under high workload. Differently, when performed without any additional task, they were categorized under the low workload.…”

“…As a first contribution, we introduce a general profiling framework applicable to different virtual devices (e.g., VR, AR), applied fields (e.g., everyday use cases, work scenarios), and types of user behaviors (e.g., walking, searching, pointing). We test our framework on data from our previous works [11], [12], showing the generability of the approach. Since such previous studies revealed gender differences under diverse workload conditions, we additionally investigate the workload impact on profiling.…”

“…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.…”

“…As part of the VR experiment, participants guided a virtual replica of an industrial robotic arm (Universal Robot e-Series UR5e) developed in Unity [12]. They were equipped with an HTC VIVE Pro Eye VR device and two VR controllers and guided the robotic arm shown in figure 2b through a pickand-place, i.e., picking and placing a bolt into a box.…”

“…In the VR scenario, participants were only moving their upper body, and in the high workload conditions they were additionally engaged in a secondary mental task. We know from the literature that a higher workload is related to higher changes in eye behavior [12]. Therefore, the VRembedded eye-tracker might have had an essential impact on the identification performance, mainly when users were under higher mental strain rather than when performing less demanding tasks (i.e., CT-Low, AT-Low).…”