FEAST：面向真实环境个性化的灵活进餐辅助系统
FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization
AI总结：
本文提出灵活进餐辅助系统 FEAST，通过模块化硬件、多样化交互及基于大语言模型的安全可调行为树，实现真实环境下的个性化定制，在多场景用户研究中验证了其优于固定基线的适应性与生态效度。
摘要：
物理护理机器人有望改善全球数百万需要进食辅助人群的生活质量。然而，由于部署期间出现的活动（如进食、饮水、擦嘴）、情境（如社交、看电视）、食物种类和用户偏好的多样性，家庭进餐辅助仍然面临挑战。在本研究中，我们提出了 FEAST，这是一种灵活的进餐辅助系统，可以在真实环境中进行个性化定制，以满足个体护理接受者的独特需求。该系统由两名社区研究人员协作开发，并基于对多样化护理接受者群体的形成性研究，遵循真实环境个性化的三个关键原则：适应性、透明度和安全性。FEAST 通过以下方式体现这些原则：(i) 模块化硬件，支持在辅助进食、饮水和擦嘴之间切换；(ii) 多样化的交互方式，包括网页界面、头部手势和物理按钮，以适应不同的功能能力和偏好；(iii) 参数化行为树，可使用大语言模型进行安全且透明的调整。我们根据形成性研究中确定的个性化需求评估了该系统，结果表明 FEAST 提供了广泛且透明的安全调整，并优于仅限于固定定制的最先进基线。为了展示实际适用性，我们与两名护理接受者（同时也是社区研究人员）进行了家庭内用户研究，在三种不同场景下分别为他们提供了三餐。我们进一步通过让一名此前不熟悉该系统的职业治疗师进行评估，来考察 FEAST 的生态效度。在所有情况下，用户都成功地对 FEAST 进行了个性化定制，以满足其个人需求和偏好。网站：https://emprise.cs.cornell.edu/feast
英文摘要：
Physical caregiving robots hold promise for improving the quality of life of millions worldwide who require assistance with feeding. However, in-home meal assistance remains challenging due to the diversity of activities (e.g., eating, drinking, mouth wiping), contexts (e.g., socializing, watching TV), food items, and user preferences that arise during deployment. In this work, we propose FEAST, a flexible mealtime-assistance system that can be personalized in-the-wild to meet the unique needs of individual care recipients. Developed in collaboration with two community researchers and informed by a formative study with a diverse group of care recipients, our system is guided by three key tenets for in-the-wild personalization: adaptability, transparency, and safety. FEAST embodies these principles through: (i) modular hardware that enables switching between assisted feeding, drinking, and mouth-wiping, (ii) diverse interaction methods, including a web interface, head gestures, and physical buttons, to accommodate diverse functional abilities and preferences, and (iii) parameterized behavior trees that can be safely and transparently adapted using a large language model. We evaluate our system based on the personalization requirements identified in our formative study, demonstrating that FEAST offers a wide range of transparent and safe adaptations and outperforms a state-of-the-art baseline limited to fixed customizations. To demonstrate real-world applicability, we conduct an in-home user study with two care recipients (who are community researchers), feeding them three meals each across three diverse scenarios. We further assess FEAST's ecological validity by evaluating with an Occupational Therapist previously unfamiliar with the system. In all cases, users successfully personalize FEAST to meet their individual needs and preferences. Website: https://emprise.cs.cornell.edu/feast
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
