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

Fanjun Bu, Kerstin Fischer, Wendy Ju

TL;DR

The paper addresses how people make sense of robots in public spaces by analyzing a field deployment of two trash-barrel robots in Astor Place, NYC, using 274 interactions and 65 interviews. It applies thematic analysis to interviews and video-grounded analysis to capture observable sensemaking, revealing that people draw on deployment context, cultural knowledge, and social considerations beyond the robot's mechanics. Key contributions include a nuanced understanding of resources used in public sensemaking, and rich design implications for signaling ownership, purpose, data usage, and contextual cues to foster appropriate public engagement. The work advances in-field methodology for sensemaking analysis and offers actionable guidance for designers and researchers deploying robots in urban settings.

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.

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

TL;DR

The paper addresses how people make sense of robots in public spaces by analyzing a field deployment of two trash-barrel robots in Astor Place, NYC, using 274 interactions and 65 interviews. It applies thematic analysis to interviews and video-grounded analysis to capture observable sensemaking, revealing that people draw on deployment context, cultural knowledge, and social considerations beyond the robot's mechanics. Key contributions include a nuanced understanding of resources used in public sensemaking, and rich design implications for signaling ownership, purpose, data usage, and contextual cues to foster appropriate public engagement. The work advances in-field methodology for sensemaking analysis and offers actionable guidance for designers and researchers deploying robots in urban settings.

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.

Paper Structure

This paper contains 48 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Public deployments of robots
  4. Sensemaking in Human-Robot Interaction
  5. Resources of Sensemaking
  6. Previous Work on Trash Barrel Robots
  7. Method
  8. Data Collection
  9. Site
  10. System
  11. Data
  12. Wizard
  13. Interviews
  14. Analysis
  15. Interview Analysis
  16. ...and 33 more sections

Figures (3)

  • Figure 1: People's assumption of whether the robot is pragmatic or imperative may influence their interaction strategy with the robot.
  • Figure 2: "I don't know why it's like that. Before you came, we were asking, is it a joke?" While not always obvious, people's interactions reflect their beliefs about the deployment of robot trash barrels. Here, a French couple who were unfamiliar with the signage in the U.S. thought the deployment was a prank, and they played along. "Why one is blue, one is grey, for which one is it, you know, recycled or not recycled. We don't know. Me personally, I don't know. I don't know the difference between the two of them."
  • Figure 3: Results from thematic coding on the interview transcriptions. The x-axis represents the number of sentences from the interview that are coded with the corresponding theme.