Robot-Assisted Social Dining as a White Glove Service
This work addresses the challenge of enabling people with disabilities to dine independently and with dignity in social settings, though it is incremental as it builds on existing robot-assisted feeding systems by extending them to new contexts.
The paper tackled the problem of robot-assisted feeding in social dining contexts like restaurants, which are unexplored compared to lab or home settings, by using participatory design to uncover ideal scenarios and propose that such systems should embody white glove service principles.
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.