Robots that use gestures in conjunction with speech can achieve more effective and natural communication with human teammates, however, not all robots have capable and dexterous arms. Augmented Reality technology has effectively enabled deictic gestures for morphologically limited robots in prior work, however, the design space of AR-facilitated iconic gestures remains under-explored. Moreover, existing work largely focuses on closed-world context, where all referents are known a priori. In this work, we present a human-subject study situated in an open-world context, and compare the task performance and subjective perception associated with three different iconic gesture designs (anthropomorphic, non-anthropomorphic, deictic-iconic) against previously studied abstract gesture design. Our quantitative and qualitative results demonstrate that deictic iconic gestures (in which a robot hand is shown pointing to a visualization of a target referent) outperforms all other gestures on all metrics – but that non-anthropomorphic iconic gestures (where a visualization of a target referent appears on its own) is overall most preferred by users. These results represent a significant step forward to enabling effective human-robot interactions in realistic large-scale open-world environments.

In this work, we present Robots for Social Justice (R4SJ): a framework for an equitable engineering practice of Human-Robot Interaction, grounded in the Engineering for Social Justice (E4SJ) framework for Engineering Education and intended to complement existing frameworks for guiding equitable HRI research. To understand the new insights this framework could provide to the field of HRI, we analyze the past decade of papers published at the ACM/IEEE International Conference on Human-Robot Interaction, and examine how well current HRI research aligns with the principles espoused in the E4SJ framework. Based on the gaps identified through this analysis, we make five concrete recommendations, and highlight key questions that can guide the introspection for engineers, designers, and researchers. We believe these considerations are a necessary step not only to ensure that our engineering education efforts encourage students to engage in equitable and societally beneficial engineering practices (the purpose of E4SJ), but also to ensure that the technical advances we present at conferences like HRI promise true advances to society, and not just to fellow researchers and engineers.

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.

Robots deployed into real-world task-based environments may need to provide assistance, troubleshooting, and on-the-fly instruction for human users. While previous work has considered how robots can provide this assistance while co-located with human teammates, it is unclear how robots might best support users once they are no longer co-located. We propose the use of Augmented Reality as a medium for conveying long-distance task guidance from humans’ existing robot teammates, through Augmented Reality facilitated Robotic Guidance (ARRoG). Moreover, because there are multiple ways that a robot might project its identity through an Augmented Reality Head Mounted Display, we identify two candidate designs inspired by existing interaction patterns in the human-robot interaction (HRI) literature (re-embodiment-based and telepresence-based identity projection designs), present the results of a design workshop to explore how these designs might be most effectively implemented, and the results of a human-subject study intended to validate these designs.

Augmented Reality (AR) technologies present an exciting new medium for human-robot interactions, enabling new opportunities for both implicit and explicit human-robot communication. For example, these technologies enable physically-limited robots to execute non-verbal interaction patterns such as deictic gestures despite lacking the physical morphology necessary to do so. However, a wealth of HRI research has demonstrated real benefits to physical embodiment (compared to, e.g., virtual robots on screens), suggesting AR augmentation of virtual robot parts could face challenges. In this work, we present empirical evidence comparing the use of virtual (AR) and physical arms to perform deictic gestures that identify virtual or physical referents. Our subjective and objective results demonstrate the success of mixed reality deictic gestures in overcoming these potential limitations, and their successful use regardless of differences in physicality between gesture and referent. These results help to motivate the further deployment of mixed reality robotic systems and provide nuanced insight into the role of mixed-reality technologies in HRI contexts.