FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization
Rajat Kumar Jenamani, Tom Silver, Ben Dodson, Shiqin Tong, Anthony Song, Yuting Yang, Ziang Liu, Benjamin Howe, Aimee Whitneck, Tapomayukh Bhattacharjee

TL;DR
FEAST is a customizable, safe, and transparent mealtime-assistance system for caregiving robots, enabling in-the-wild personalization to accommodate diverse user needs, preferences, and contexts through modular hardware and adaptable behavior models.
Contribution
This work introduces FEAST, a novel flexible system that allows real-time personalization of robot-assisted feeding through modular hardware, diverse interaction methods, and behavior adaptation using large language models.
Findings
FEAST successfully personalizes assistance to individual needs.
The system outperforms fixed customization baselines.
Users can safely and transparently adapt FEAST in real-world settings.
Abstract
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,…
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Taxonomy
TopicsMobile Health and mHealth Applications · Innovative Human-Technology Interaction · Context-Aware Activity Recognition Systems
