There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts.

The perception of danger in HRI settings has become increasingly important as interactions between robots and humans become more commonplace. Previously, a perceived danger scale was developed and validated. Here, we shortened this scale to create the Perceived Danger-Short Form (PD-SF) scale. Experiment 1 used pre-existing data and standard procedures to shorten the scale from 12 items to 4. Experiment 2 validated the short form in a new experiment where participants observed images of robots holding kitchen items of varying levels of danger in close proximity to a human. PD-SF was able to capture differences across the kitchen items. Results from both experiments indicate that PD-SF is a reliable and psychometrically valid measure of perceived danger in HRI contexts.

The perception of agency in human robot interaction has become increasingly important as robots become more capable and more social. There are, however, no accepted or consistent methods of measuring perceived agency; researchers currently use a wide range of techniques and surveys. We provide a definition of perceived agency, and from that definition we create and psychometrically validate a scale to measure perceived agency. We then perform a scale evaluation by comparing the PA scale constructed in experiment 1 to two other existing scales. We find that our PA and PA-R (Perceived Agency–Rasch) scales provide a better fit to empirical data than existing measures. We also perform scale validation by showing that our scale shows the hypothesized relationship between perceived agency and morality.

The perception of agency in robots and AI characters has become increasingly important as different agents increase their capabilities. Experiment 1 took an existing measure of perceived agency and created a reduced version by using existing Rasch item reduction measures; Eight and five item scales were created. Experiment 2 showed that all three scales (PA, PA8, PA5) were able to capture differences in perceived agency between a cheating robot (higher PA) and a non-cheating robot (lower PA). Experiment 3 showed that all three scales were able to show the predicted positive relationship between perceived agency and perceived moral agency. All three scales also showed high internal validity. Suggestions for the usage of the scales was also discussed.

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Prior research in human-robot teamwork proposes fairness definitions for human-robot teaming that are based on the work completed by each team member. However, metrics that properly capture people’s perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people’s perception of fairness. First, we extend prior fairness metrics based on team members’ capabilities and workload to a bigger team. We also develop a new metric to quantify the amount of time that the robot spends working on the same task as each person. We conduct an online user study (n=95) and show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people’s assessment of fairness. When asked to rate fairness based on the amount of time that the robot spends working with each person, participants used two factors (fairness based on the robot’s time and teammates’ capabilities). This bleed-over effect is stronger when people are asked to assess fairness based on capability. From these insights, we propose design guidelines for algorithms to enable robotic teammates to consider fairness in its decision-making to maintain positive team social dynamics and team task performance.

Social perceivers often view a human agent's norm-violating behavior as diagnostic of that person's mental states, while behaviors that conform to norms are viewed as less informative. We developed a series of stimulus videos depicting a DRC-HUBO robot engaging in norm-violating and norm-conforming behaviors. We explored the hypothesis that robots' norm-violating actions may invite social perceivers to increase their mental state attributions in a similar manner as they do in humans. Surprisingly, we found that norm-conforming behaviors appear to be at least as conducive as norm-violating behaviors, and perhaps even moreso, to mental state attribution to robotic agents.

Crandalland Cummings & Mitchell introduced fan-out as a measure of the maximum number of robots a single human operator can supervise in a given single-human-multiple-robot system. Fan-out is based on the time constraints imposed by limitations of the robots and of the supervisor, e.g., limitations in attention. Adapting their work, we introduced a dynamic model of operator overload that predicts failures in supervisory control in real time, based on fluctuations in time constraints and in the supervisor's allocation of attention, as assessed by eye fixations. Operator overload was assessed by damage incurred by unmanned aerial vehicles when they traversed hazard areas. The model generalized well to variants of the baseline task. We then incorporated the model into the system where it predicted in real time, when an operator would fail to prevent vehicle damage and alerted the operator to the threat at those times. These model-based adaptive cues reduced the damage rate by one-half relative to a control condition with no cues.

Adaptive automation (AA) can improve performance while addressing the problems associated with a fully automated system. The best way to invoke AA is unclear, but two ways include critical events and the operator's state. A hybrid model of AA invocation, the dynamic model of operator overload (DMOO), that takes into account critical events and the operator's state was recently shown to improve performance. The DMOO initiates AA using critical events and attention allocation, informed by eye movements. We compared the DMOO with an inaccurate automation invocation system and a system that invoked AA based only on critical events. Fewer errors were made with DMOO than with the inaccurate system. In the critical event condition, where automation was invoked at an earlier point in time, there were more memory and planning errors, while for the DMOO condition, which invocated automation at a later point in time, there were more perceptual errors. These findings provide a framework for reducing specific types of errors through different automation invocation.

For a single operator to effectively control multiple robots, operator situation awareness is a critical component of the human-robot system. There are three levels of situation awareness: perception, comprehension, and projection into the future [1]. We focus on the perception level to develop a theoretic model of the perceptual-cognitive processes underlying situation awareness. Eye movement measures were developed as indicators of cognitive processing and these measures were used to account for operator situation awareness on a supervisory control task. The eye movement based model emphasizes the importance of visual scanning and attention allocation as the cognitive processes that lead to operator situation awareness and the model lays the groundwork for real-time prediction of operator situation awareness.

A 3D optical flow tracking system was used to track participants as they watched a series of boring videos. The video stream of the participants was rated for boredom events. Ratings and head position data were combined to predict boredom events.

A postcompletion error is a distinct type of procedural error where one fails to complete the final step of a task. While redesigning interfaces and providing explicit cues have been shown to be effective in reducing the postcompletion error rate, these methods are not always feasible or well liked. This paper demonstrates how specific eye movement measures can be used to predict when a user will make a postcompletion error. We describe a real-time eye gaze system that provides cues to the user if and only if there is a high probability of the user making a postcompletion error.