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.

翻译：机器人辅助进食使需要就餐协助的残障人士能够独立且有尊严地享受用餐。然而，现有系统仅在实验室或家庭环境中经过测试，对真实社交餐饮场景（如餐厅）的探索仍显不足。为此类场景设计机器人面临独特挑战，例如机器人需应对动态且无监督的餐饮环境。通过结合半结构化访谈和基于人工智能的定制视觉故事板工具，我们与残障人士开展思辨式参与式设计，揭示了真实社交餐饮场景的理想方案。我们的核心见解表明，此类系统应体现白手套服务原则，即机器人需：（1）支持多模态输入与非侵入式输出；（2）具备情境感知的社交行为并优先响应用户需求；（3）拓展喂食以外的服务角色；（4）适应餐桌上的其他社交关系。本研究对机器人辅助进食在真实场景及群体环境中的应用具有重要启示。