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.

In this paper, we study the problem of gesture recognition as a method for divers to communicate with an underwater robot. Gesture is a common method of communication between divers, and yet autonomous underwater vehicles have very limited capacity to understand gesture given lighting and visibility constraints (e.g., from water turbidity and diver depth). Traditional deep learning methods are limited in this domain because of a lack of sufficient training data. We show that it is not enough to learn a gesture in a laboratory setting, because the appearance changes dramatically underwater. We show how hyperdimensional computing can solve this problem by permitting hypervectors to serve as abstract representations of gestures, yielding rapid adaptation to new environments and new gestures. We experimentally verify this approach using a novel dataset of 6 diving relevant gestures. We show that we can accurately adapt to a gesture learned in a laboratory setting to work with a gesture observed underwater. Our approach compares favorably to a ResNet-18, which performs well in laboratory conditions (91.9% accuracy), but performs poorly underwater (53.9% accuracy). Our proposed approach is capable of rapid adaptation, resulting in an accuracy of 83.8% on underwater gestures with just one additional example from each class added to the support set. Finally, we also show the ability to adapt to new gestures not present in our original training set. We use hypervectors to learn new gestures from the Sign Language MNIST dataset, providing a high level of accuracy with a limited amount of training data.

This paper demonstrates the effectiveness of learning based models for accurate, and reliable robot to human handoffs in various HRI scenarios. Specifically we bench marked a neuromorphic spiking neural network and a time series k-nearest neighbors classifier against traditional hand crafted force threshold methods. These models use linear force in the x, y, and z direction, as well as torque about the x, y, and z axis at the end effector of the robot arm to make handoff predictions. This paper demonstrates that these learning based methods are more robust to noise which occurs during operational use. We applied our algorithms to both stationary handoffs (stationary robot) and moving handoffs (robot walking). We believe that our evaluation is the first to examine walking handoffs. We evaluated all models in tests which determined the accuracy, precision, recall, f1, and average execution time for handoff events, noise events, and no event tests. We find that the SLAYER spiking neural network model performed the best across both walking and stationary handoffs for the majority of the evaluation criteria. Our results suggest that neuromorphic spiking neural networks are strong contenders for applications in time series, event based HRI applications.