As mobile robots become more common in shared public spaces, understanding how their movements affect pedestrian comfort has become a key challenge in human–robot interaction. Although prior research has investigated the effects of robot lateral distance and speed, findings have been inconsistent, with little attention paid to how these factors interact with pedestrians’ mobility states (e.g., walking vs. stopping). To address this gap, we conducted a virtual reality experiment where participants experienced 30 scenarios combining five lateral distances, three robot velocities, and two pedestrian states. Discomfort was measured using standardized questionnaires and real-time button presses. Results showed that discomfort increased at closer distances and higher speeds, with these effects amplified when pedestrians were walking. Discomfort was minimized when robots-maintained distances over 120 cm and moved slower than typical walking speed. This study offers new insights into how robot proxemics and pedestrian mobility jointly shape discomfort, informing proximity-aware navigation design.

Personal intelligent agents (IAs) are increasingly embedded in everyday life, a trend accelerated by generative AI technologies. Despite their growing presence, these agents often remain fragmented across different life domains and environments. This workshop explores how to design integrated IA ecosystems emphasizing continuity, coordination, and human-centered values. Participants with varied perspectives will collaboratively develop frameworks, scenarios, and guidelines for cohesive personal agent systems that enrich user experiences holistically. By examining factors that shape users' preferences for information integration or separation, we aim to inform the design of coherent, user-aligned multi-agent systems.

This study addresses the challenge of ensuring pedestrian safety in shared spaces during human-robot interactions. As mobile robots become increasingly integrated into public environments, there is a need to better understand how pedestrians perceive robots in proximity. This research explores the effects of robot lateral distance, robot velocity, and pedestrian state on pedestrian perception, with a particular focus on proximity zones during human-robot interactions. Conducted in a virtual reality environment with a within-subject design, the study measured perceived danger through button presses and visualized proximity zones using contour density plots. Findings reveal that increasing lateral distance significantly decreases button press likelihood, particularly beyond 160 cm. Robot velocities of 1 mls and 1.5 mls showed no significant difference, suggesting pedestrians perceive them similarly due to their alignment with average walking speed. Proximity zones were consistently wider and denser on the left than the right, highlighting spatial asymmetry in perception. These results provide quantitative and visual insights for designing mobile robots in shared spaces. Future research should examine environmental and group dynamics and robot design to enhance safety and public acceptance in human-robot interactions.

Conditionally automated driving systems face two main safety challenges: the inability to autonomously handle all situations the vehicle encounters, and the allowed inattention of drivers during these critical moments. Our study focuses on enhancing drivers’ situation awareness at such times by embedding information about system status and the road environment in the visual signals displayed when control is transferred from the automated driving system. Six visual signals, each including different levels of situation awareness information, were compared to examine how they influence drivers’ levels of situation awareness in a simulated environment. The results show that signals incorporating higher levels of situation awareness information about the environment significantly facilitate the recovery of situation awareness after engaging in non-driving related tasks. This research provides insights into how visual cues can be optimized to facilitate quicker recovery of situation awareness for drivers transitioning from non-driving tasks in conditionally automated vehicles.

The purpose of this study was to explore the unit of information and the size of the unit for designing a voice user interface. Through two experiments, this study investigated what form the information (the unit of information) should take and what size of that (the size of unit) should be when people were provided information by voice interfaces. Participants were presented with a task to recall (OX quiz) by listening to and remembering information (based on an encyclopedia) provided by smart speakers. In Experiment 1, it was revealed that participants stored information in their memory span on a sentence-by-sentences basis to determine how much information they could remember. In Experiment 2, sentence-based information was presented in various sizes, and participants evaluated 17 information units consisting of up to nine words as their memory limit. This information unit-based voice interface design could help improve users’ memory performance and usability.