In the Wizard-of-Oz study paradigm, human "wizards" perform not-yet-implemented system behavior, simulating, among others, how autonomous robots could interact in public to see how unwitting bystanders respond. This paper analyzes a 60-minute video recording of two wizards in a public plaza who are operating two trash-collecting robots within their line of sight. We take an ethnomethodology and conversation analysis perspective to scrutinize interactions between the wizards and the people in the plaza, focusing on critical instances where one robot gets stuck and requires collaborative intervention by the wizards. Our analysis unpacks how the wizards deal with emergent problems by pushing one robot into the other, how they manage front and backstage interactions, and how they monitor the location of each other's robots. We discuss how scrutinizing the work of wizards can inform explorative Wizard-of-Oz paradigms, the design of multi-agent robot systems, and the operation of urban robots from a distance.

In this work, we analyze video data and interviews from a public deployment of two trash barrel robots in a large public space to better understand the sensemaking activities people perform when they encounter robots in public spaces. Based on an analysis of 274 human–robot interactions and interviews with N = 65 individuals or groups, we discovered that people were responding not only to the robots or their behavior, but also to the general idea of deploying robots as trashcans, and the larger social implications of that idea. They wanted to understand details about the deployment because having that knowledge would change how they interact with the robot. Based on our data and analysis, we have provided implications for design that may be topics for future human–robot design researchers who are exploring robots for public space deployment. Furthermore, our work offers a practical example of analyzing field data to make sense of robots in public spaces.

We have developed a Communal eXtended-Reality (CXR) system that enables groups of people in a shared moving vehicle to view a common geo-synchronized tour. This paper describes the geo-synchronized multi-user extended reality system we created to provide a situated and shared experience to promote community engagement. This paper describes: (a) the technical implementation of the CXR system, which geo-locates and orients the view of the participant within the moving vehicle; (b) the immersive digital twin tour, critically aligned with the real-life location; (c) our fallback system, which allows people who feel disoriented or motion-sick to continue along with the content of the tour. We validated the sense of communality, comfort, and effectiveness of the system through in-ride observation and post-ride surveys. Our intent is to enable development of similar systems to foster communal engagement in communities worldwide.

The introduction of robots in public spaces raises many questions concerning emergent interactions with robots. In this paper, we use video analysis to study two robotic trashcans deployed in a busy city square. We focus on the movement-based practices that emerged between the robot, the robot operators, and the inhabitants of the square. These practices spanned ways of attracting the robot and disposing of trash, the robot ’asking’ for trash, ’demonstrations’ by those in the square, as well as passersby in the square navigating around and in coordination with the robots. In discussion, we document these ’spontaneous simple sequential systematics’ - interactions that were systematic (they had an order), sequential (they had parts that happened one at a time), simple (in that they could be understood and copied by an observer) and spontaneous (they could be produced with no prompting or training). Building on this we discuss how we might think of robotic motion as a design space, along with HCI contributions to urban robotics.

In automotive user interface design, testing often starts with lab-based driving simulators and migrates toward on-road studies to mitigate risks. Mixed reality (XR) helps translate virtual study designs to the real road to increase ecological validity. However, researchers rarely run the same study in both in-lab and on-road simulators due to the challenges of replicating studies in both physical and virtual worlds. To provide a common infrastructure to port in-lab study designs on-road, we built a platform-portable infrastructure, Portobello, to enable us to run twinned physical-virtual studies. As a proof-of-concept, we extended the on-road simulator XR-OOM with Portobello. We ran a within-subjects, autonomous-vehicle crosswalk cooperation study (N=32) both in-lab and on-road to investigate study design portability and platform-driven influences on study outcomes. To our knowledge, this is the first system that enables the twinning of studies originally designed for in-lab simulators to be carried out in an on-road platform.

High-fidelity driving simulators can act as testbeds for designing in-vehicle interfaces or validating the safety of novel driver assistance features. In this system paper, we develop and validate the safety of a mixed reality driving simulator system that enables us to superimpose virtual objects and events into the view of participants engaging in real-world driving in unmodified vehicles. To this end, we have validated the mixed reality system for basic driver cockpit and low-speed driving tasks, comparing the use of the system with non-headset and with the headset driving conditions, to ensure that participants behave and perform similarly using this system as they would otherwise. This paper outlines the operational procedures and protocols for using such systems for cockpit tasks (like using the parking brake, reading the instrument panel, and turn signaling) as well as basic low-speed driving exercises (such as steering around corners, weaving around obstacles, and stopping at a fixed line) in ways that are safe, effective, and lead to accurate, repeatable data collection about behavioral responses in real-world driving tasks.