Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015–2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges.

When children are anxious or scared, it can be hard for them to stay still or follow instructions during medical procedures, making the process more challenging and affecting procedure results. This is particularly true for radiological procedures, where long scan times, confined spaces, and loud noises can cause children to move, significantly impacting scan quality. To this end, sometimes children are sedated, but doctors are constantly seeking alternative non-pharmacological solutions. This work aims to explore how social robots could assist in preparing children for radiological procedures. We have conducted a focus group discussion with five hospital stakeholders, namely radiographers, pediatricians, and clinical engineers, to explore (i) the context regarding children’s preparation for radiological procedures, hence their needs and how children are currently prepared, and (ii) the potential role of social robots in this process. The discussion was transcribed and analysed using thematic analysis. Among our findings, we identified three potential roles for a social robot in this preparation process: offering infotainment in the waiting room, acting as a guide within the hospital, and assisting radiographers in preparing children for the procedure. We hope that insights from this study will inform the design of social robots for pediatric healthcare.

From RViz to augmented reality (AR), a wide variety of robot signal visualizations exist for conveying robot capabilities. Many of the visualizations designed for AR, however, have not isolated multiple salient Virtual Design Elements (VDEs) for a given signal and comparatively evaluated combinations of those VDEs. To address this, we identify multiple VDEs for AR signaling of the following core robot capabilities: navigation, light detection and ranging (LiDAR), camera, face detection, audio localization, and natural language processing. We evaluated each signal's VDE combinations with an Amazon Mechanical Turk study (n=150) where participants watched 4 videos for each signal (consisting of 2 independent VDE choices) and rated the clarity and visual appeal of each signal. The results define a set of the most clear and visually appealing signal visualization designs and inform about interaction effects among VDEs. The resulting VDEs offer design insights and a baseline for continued research into AR robot capability signalling.