Robot-Assisted Social Dining as a White Glove Service

TL;DR

This work uncovered ideal scenarios for in-the-wild social dining in robot-assisted feeding through speculative participatory design with people with disabilities, supported by semi-structured interviews and a custom AI-based visual storyboarding tool.

Abstract

Robot-assisted feeding enables people with disabilities who require assistance eating to enjoy a meal independently and with dignity. However, existing systems have only been tested in-lab or in-home, leaving in-the-wild social dining contexts (e.g., restaurants) largely unexplored. Designing a robot for such contexts presents unique challenges, such as dynamic and unsupervised dining environments that a robot needs to account for and respond to. Through speculative participatory design with people with disabilities, supported by semi-structured interviews and a custom AI-based visual storyboarding tool, we uncovered ideal scenarios for in-the-wild social dining. Our key insight suggests that such systems should: embody the principles of a white glove service where the robot (1) supports multimodal inputs and unobtrusive outputs; (2) has contextually sensitive social behavior and prioritizes the user; (3) has expanded roles beyond feeding; (4) adapts to other relationships at the dining table. Our work has implications for in-the-wild and group contexts of robot-assisted feeding.

Figures (8)

• Figure 1: Study Setup. The study was conducted either at the university or at the participant's home with the same set-up.
• Figure 2: Procedure of the design study included: (1) introducing the task to the participant, (2) creating an avatar based on their lived experience to be represented in the task, (3) creating a storyboard of a generic experience of dining at a restaurant, (4) zooming into 9 key moments (indicated in blue), (5) creating an imagined robot character, and (6) exit interviews to supplement participant's design process. During steps (3) and (4), participants self-reflected to supplement their images.
• Figure 3: Iteration of robot during speculative design. The caption captures the prompt by participant to create scenes.
• Figure 4: Participants' viewpoints on the Interaction Ecology in Robot-Assisted Social Dining described in Section \ref{['RQ1-results']}. Further details can be seen in Table \ref{['tab:participant-coms']}, \ref{['tab:rq1-acc-table-a']}, and \ref{['tab:rq1-acc-table-b']} in Appendix \ref{['appendix:figs-for-acc']}
• Figure 5: Participants' perspective on context-sensitive robot behavior in social dining was generally in agreement. The four images within this figure were created by the participants using Speak2Scene, where each image captures some ideas. Further details can be seen in Appendix \ref{['appendix:figs-for-acc']}.