Robot-Assisted Social Dining as a White Glove Service

TL;DR

This paper explores designing socially aware robot-assisted feeding systems for real-world dining environments, emphasizing user dignity, social context, and adaptability through participatory design insights.

Contribution

It introduces a set of design principles for social robots in dining contexts, based on participatory research with users and innovative visual storyboarding tools.

Findings

Identified key design principles for social robots in dining environments.

Proposed a white glove service model for robot-assisted feeding.

Highlighted the importance of context-sensitive social behavior.

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.