Design fixation, a phenomenon describing designers' adherence to pre-existing ideas or concepts that constrain design outcomes, is particularly prevalent in human-robot interaction (HRI), for example, due to collectively held and stabilised imaginations of what a robot should look like or behave. In this paper, we explore the contribution of creative AI tools to overcome design fixation and enhance creative processes in HRI design. In a four weeks long design exploration, we used generative text-to-image models to ideate and visualise robotic artefacts and robot sociotechnical imaginaries. We exchanged results along with reflections through a digital postcard format. We demonstrate the usefulness of our approach to imagining novel robot concepts, surfacing existing assumptions and robot stereotypes, and situating robotic artefacts in context. We discuss the contribution to designerly HRI practices and conclude with lessons learnt for using creative AI tools as an emerging design practice in HRI research and beyond.

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

As mobile robots enter shared urban spaces, operating in close proximity to people, this raises new challenges in terms of how these robots communicate with passers-by. Following an iterative process involving expert focus groups (n=8), we designed an augmented reality concept that visualises the robot's navigation intent and the pedestrian's predicted path. To understand the impact of path visualisations on trust, sense of agency, user experience, and robot understandability, we conducted a virtual reality evaluation (n=20). We compared visualising both robot intent and pedestrian path prediction against just visualising robot intent and a baseline without augmentation. The presence of path visualisations resulted in a significant improvement of trust. Triangulation of quantitative and qualitative results further highlights the impact of pedestrian path prediction visualisation on robot understandability as it allows for exploratory interaction.

Policymakers advocate for the use of external Human-Machine Interfaces (eHMIs) to allow autonomous vehicles (AVs) to communicate their intentions or status. Nonetheless, scalability concerns in complex traffic scenarios arise, such as potentially increasing pedestrian cognitive load or conveying contradictory signals. Building upon precursory works, our study explores ‘interconnected eHMIs,’ where multiple AV interfaces are interconnected to provide pedestrians with clear and unified information. In a virtual reality study (N=32), we assessed the effectiveness of this concept in improving pedestrian safety and their crossing experience. We compared these results against two conditions: no eHMIs and unconnected eHMIs. Results indicated interconnected eHMIs enhanced safety feelings and encouraged cautious crossings. However, certain design elements, such as the use of the colour red, led to confusion and discomfort. Prior knowledge slightly influenced perceptions of interconnected eHMIs, underscoring the need for refined user education. We conclude with practical implications and future eHMI design research directions.

In this article, we report on the design and evaluation of an external human-machine interface (eHMI) for a real autonomous vehicle (AV), developed to operate as a shared transport pod in a pedestrianized urban space. We present insights about our human-centered design process, which included testing initial concepts through a tangible toolkit and evaluating 360-degree recordings of a staged pick-up scenario in virtual reality. Our results indicate that in complex mobility scenarios, participants filter for critical eHMI messages; further, we found that implicit cues (i.e., pick-up manoeuvre and proximity to the rider) influence participants' experience and trust, while at the same time more explicit interaction modes are desired. This highlights the importance of considering interactions with shared AVs as a service more holistically, in order to develop knowledge about AV-pedestrian interactions in complex mobility scenarios that complements more targeted eHMI evaluations.

The increasing deployment of robots in urban spaces calls for design strategies to ensure their adaptation and to mitigate potential disruptions to complex urban contexts. Our research aims to initiate the discussion of contextual adaptability issues of urban robots by exploring everyday scenarios where their presence would appear out of place. We created a design probe for people to carry in their daily lives, facilitating them to envision the robot’s presence and capture scenarios where a robot seems to be disruptive. We collected data by distributing the probes among the research team and conducting a city walk activity using the probe at a workshop. This paper presents factors arising from the collected scenarios, encompassing temporal, spatial, cultural, and social dynamics, as well as various stakeholders that robots need to adapt to. These findings provide a blueprint and potential research directions for future research into robot contextual adaptability in urban environments.