Robot-Assisted Social Dining as a White Glove Service
2026-02-17 • Robotics
RoboticsArtificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors studied how robots can help people with disabilities eat by themselves in real social places like restaurants, not just at home or in labs. They found that for robots to work well in these busy, changing settings, they should offer polite, flexible help that respects the user and adapts to the social situation. The robot should also do more than just feed, like fitting in naturally with different people at the table. Their findings help design better robots for eating support in everyday social dining.
robot-assisted feedingparticipatory designsocial diningmultimodal inputscontext-aware robotsAI visual storyboardingdisability assistancehuman-robot interactionadaptive behaviorin-the-wild testing
Authors
Atharva S Kashyap, Ugne Aleksandra Morkute, Patricia Alves-Oliveira
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.