While resuscitation training is critical, healthcare workers (HCWs) with high workload have limited chance to get trained and re-trained due to time and resource constraints. To address this gap, we engaged in a co-design process of robots that facilitate and prepare HCWs for resuscitation procedures (i.e., codes). First, we investigated what resuscitation training consists of, including challenges faced by trainees and trainers. Second, we collaboratively explored how a crash cart robot, that guides users to medical supplies and equipment, could assist trainers and trainees synchronously–during team-based clinical simulations and asynchronously–during one-on-one training. We found that robots could 1) serve as a learning assistant by providing real-time feedback and supporting personalized training needs; and 2) an evaluating assistant by monitoring multiple trainees and tracking critical timing of interventions in the training. Through this new training paradigm, we hope to demonstrate opportunities for crash cart robots to aid HCWs for their sustainable training and reskilling. We discuss the role of robots in training beyond cognitive knowledge, situating them within two underexplored contexts: practical skill training and team-based training.

In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced environments, facilitating effective communication among team members, and adapting to rapidly changing situations. While teleoperated robots have been successfully used in high-stakes domains such as firefighting and space exploration, autonomous robots that aid high-stakes teamwork remain underexplored. To address this gap, we conducted a rapid prototyping process to develop a series of seemingly autonomous robots designed to assist clinical teams in the Emergency Room. We transformed a standard crash cart-which stores medical equipment and emergency supplies into a medical robotic crash cart (MCCR). The MCCR was evaluated through field deployments to assess its impact on team workload and usability, identified taxonomies of failure, and refined the MCCR in collaboration with healthcare professionals. Our work advances the understanding of robot design for high-stakes, time-sensitive settings, providing insights into useful MCCR capabilities and considerations for effective human-robot collaboration. By publicly disseminating our MCCR tutorial, we hope to encourage HRI researchers to explore the design of robots for high-stakes teamwork.

This paper introduces CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, designed to capture continuous affect data about interpersonal perceptions in dyadic interactions. Grounded in behavioral ecology perspectives of emotion, this approach replaces valence as the relevant rating dimension with approach and withdrawal, reflecting the degree to which behavior is perceived as increasing or decreasing social distance. We conducted a study to experimentally validate the efficacy of our platform with 24 participants. The tool’s effectiveness was tested in the context of dyadic negotiation, revealing insights about how interpersonal dynamics evolve over time. We find that the continuous affect rating method is consistent with individuals’ perception of the overall interaction. This paper contributes to the growing body of research on affective computing and offers a valuable tool for researchers interested in investigating the temporal dynamics of affect and emotion in social interactions.

We present an accessible robot interface for telemanipulation (A-RIFT), which preserves the haptic channel partially in a zero-additional-cost interface by visual substitution of force feedback (VSFF). This work explores a gap in the literature, resulting from the focus on performance improvements in telerobotics at increasing interface costs. Unlike most telemanipulation interfaces for high-degree-of-freedom robotic systems, this one requires minimal training and can be run in a web browser under high latency conditions, using an Internet connected computer with the user's own mouse and keyboard. To evaluate the performance of the system, we ran a controlled user study (N=12) to test how different distances (local vs. remote) and VSFF (on vs. off) affect the system's usability. As expected, participants in remote conditions performed worse than those in closer proximity. Despite several participants claiming that the visual display of force feedback did not help them, our analysis of their task performance showed that operators in remote condition actually performed statistically significantly better with the visual force feedback display than without it. These results indicate a promising new interface design direction for low-cost telemanipulation.