When humans supervise multiple semi-autonomous robots while also attending to their own tasks simultaneously, they may lack the situation awareness needed to assist their robot teammates. There is a need to monitor the human’s situation awareness in real-time, so interventions can be taken to improve poor situation awareness. While prior work has developed models to estimate human situation awareness, they rely heavily on advanced machine learning models and a single source of input through eye-tracking that can pose operational challenges. We develop a real-time human situation awareness estimator based on data from a human-robot teaming experiment. The situation awareness estimator uses simple and interpretable logistic regression models that take inputs from both eye-tracking and behavioral measures. Cross-validation demonstrated the situation awareness estimator had an average accuracy of 74%. The estimator is robust to missing inputs, and can monitor human situation awareness non-intrusively in real-time.

The rapid deployment of security robots across our society calls for further examination of their acceptance. This study explored human acceptance of security robots by theoretically extending the technology acceptance model to include the impact of autonomy and risk. To accomplish this, an online experiment involving 236 participants was conducted. Participants were randomly assigned to watch a video introducing a security robot operating at an autonomy level of low, moderate, or high, and presenting either a low or high risk to humans. This resulted in a 3 (autonomy) × 2 (risk) between-subjects design. The findings suggest that increased perceived usefulness, perceived ease of use, and trust enhance acceptance, while higher robot autonomy tends to decrease acceptance. Additionally, the physical risk associated with security robots moderates the relationship between autonomy and acceptance. Based on these results, this paper offer recommendations for future research on security robots.