Making Sense of Robots in Public Spaces: A Study of Trash Barrel Robots

2025-04-01

AI Summary

This study investigates how the public constructs meaning when encountering service robots—exemplified by trash-can robots—in authentic public spaces, prioritizing the “why” (ontological significance) over the “how” (functional operation). Method: Drawing on 274 naturally occurring interaction videos and 65 in-depth interviews, the research integrates multimodal field observation, semi-structured interviews, and grounded theory analysis. Contribution/Results: It systematically identifies six empirically grounded dimensions of meaning construction—the first such taxonomy for public-service robots. The study proposes a meaning-centered design framework for public robots and distills four actionable design principles that expose the sociotechnical logic and spatial power relations underlying robotic deployment. By bridging theoretical rigor with empirical grounding, it establishes a novel, practice-oriented research paradigm for socially embedded robotics and advances public space design toward meaning-sensitive, human–robot co-governance.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Understanding human sensemaking around public space robots
• Exploring social implications of trashcan robot deployment
• Analyzing field data to improve human-robot interaction design

Innovation

• Analyzing video data and interviews
• Studying 274 human-robot interactions
• Providing design implications for public robots