An official website of the United States government
Sidewalk robots are becoming increasingly common worldwide, yet their operation on public walkways presents challenges for pedestrians. This is especially true for people with motor disabilities (PWMD) who already manage obstacles such as inadequate ramps and public incivility. The addition of sidewalk robots could further intensify these difficulties, which poses an urgent need to examine how the design of sidewalk robots may influence the daily navigation experiences of PWMD. This poster illustrates findings from semi-structured interviews with ten PWMD, providing insights into their perspectives on the presence of sidewalk robots. The study uncovers potential conflicts in shared sidewalk use and the adaptive actions PWMD described needing to undertake in response. Interviewees raised concerns about whether the robots could accommodate the needs of PWMD, as compared to people walking on foot, and the repercussions of any shortcomings in this regard. Our research also examines tensions stemming from different robotic design choices, indicating the necessity for more accessible public robot designs. We further delve into PWMD’s interaction needs and modalities for routine operation and in the event of robot malfunction. As cities increasingly allow for the deployment of robots in public spaces, this work seeks to inform equitable design and deployment guidelines for sidewalk robots and calls for further research into the implications of the rise of public robots for the diverse populations that make up any given municipality.
more »
« less
This content will become publicly available on December 31, 2026
Making Sense of Robots in Public Spaces: A Study of Trash Barrel Robots
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 withN =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.
more »
« less
- Award ID(s):
- 2423127
- PAR ID:
- 10639930
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Human-Robot Interaction
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 2573-9522
- Page Range / eLocation ID:
- 1 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Much research in healthcare robotics explores ex- tending rehabilitative interventions to the home. However, for adults, little guidance exists on how to translate human-delivered, clinic-based interventions into robot-delivered, home-based ones to support longitudinal interaction. This is particularly problematic for neurorehabilitation, where adults with cognitive impairments require unique styles of interaction to avoid frustration or overstimulation. In this paper, we address this gap by exploring the design of robot-delivered neurorehabilitation interventions for people with mild cognitive impairment (PwMCI). Through a multi-year collaboration with clinical neuropsychologists and PwMCI, we developed robot prototypes which deliver cognitive training at home. We used these prototypes as design probes to understand how participants envision long-term deployment of the intervention, and how it can be contextualized to the lives of PwMCI. We report our findings and specify design patterns and considerations for translating neurorehabilitation interventions to robots. This work will serve as a basis for future endeavors to translate cognitive training and other clinical interventions onto a robot, support longitudinal engagement with home-deployed robots, and ultimately extend the accessibility of longitudinal health interventions for people with cognitive impairments.more » « less
-
Sidewalk delivery robots are being deployed as a form of last-mile delivery. While many such robots have been deployed on college campuses, fewer have been piloted on public sidewalks. Furthermore, there have been few observational studies of robots and their interactions with pedestrians. To better understand how sidewalk robots might integrate into public spaces, the City of Pittsburgh, Pennsylvania conducted a pilot of sidewalk delivery robots to understand possible uses and the challenges that could arise in interacting with people in the city. Our team conducted ethnographic observations and intercept interviews to understand how residents perceived of and interacted with sidewalk delivery robots over the course of the public pilot. We found that people with limited knowledge about the robots crafted stories about their purpose and function. We observed the robots causing distractions and obstructions with different sidewalk users (including children and dogs), witnessed people helping immobilized robots, and learned about potential accessibility issues that the robots may pose. Based on our findings, we contribute a set of recommendations for future pilots, as well as questions to guide future design for robots in public spaces.more » « less
-
All robots create consequential sound—sound produced as a result of the robot’s mechanisms—yet little work has explored how sound impacts human-robot interaction. Recent work shows that the sound of different robot mechanisms affects perceived competence, trust, human-likeness, and discomfort. However, the physical sound characteristics responsible for these perceptions have not been clearly identified. In this paper, we aim to explore key characteristics of robot sound that might influence perceptions. A pilot study from our past work showed that quieter and higher-pitched robots may be perceived as more competent and less discomforting. To better understand how variance in these attributes affects perception, we performed audio manipulations on two sets of industrial robot arm videos within a series of four new studies presented in this paper. Results confirmed that quieter robots were perceived as less discomforting. In addition, higher-pitched robots were perceived as more energetic, happy, warm, and competent. Despite the robot’s industrial purpose and appearance, participants seemed to prefer more "cute" (or "kawaii") sound profiles, which could have implications for the design of more acceptable and fulfilling sound profiles for human-robot interactions with practical collaborative robots.more » « less
-
Goal-conditioned policies, such as those learned via imitation learning, provide an easy way for humans to influence what tasks robots accomplish. However, these robot policies are not guaranteed to execute safely or to succeed when faced with out-of-distribution goal requests. In this work, we enable robots to know when they can confidently execute a user’s desired goal, and automatically suggest safe alternatives when they cannot. Our approach is inspired by control-theoretic safety filtering, wherein a safety filter minimally adjusts a robot’s candidate action to be safe. Our key idea is to pose alternative suggestion as a safe control problem in goal space, rather than in action space. Offline, we use reachability analysis to compute a goal-parameterized reach-avoid value network which quantifies the safety and liveness of the robot’s pre- trained policy. Online, our robot uses the reach-avoid value network as a safety filter, monitoring the human’s given goal and actively suggesting alternatives that are similar but meet the safety specification. We demonstrate our Safe ALTernatives (SALT) framework in simulation experiments with indoor navigation and Franka Panda tabletop manipulation, and with both discrete and continuous goal representations. We find that SALT is able to learn to predict successful and failed closed-loop executions, is a less pessimistic monitor than open- loop uncertainty quantification, and proposes alternatives that consistently align with those that people find acceptable.more » « less
