# FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization

arXiv: https://arxiv.org/abs/2506.14968 · HTML: https://mlsanity.com/paper/2506.14968/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 115 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 30 of 39)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [Language to Rewards for Robotic Skill Synthesis](https://mlsanity.com/paper/2306.08647/) (2023)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [RT-H: Action Hierarchies Using Language](https://mlsanity.com/paper/2403.01823/) (2024)
- [Yell At Your Robot: Improving On-the-Fly from Language Corrections](https://mlsanity.com/paper/2403.12910/) (2024)
- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Can Large Language Models Reason and Plan?](https://mlsanity.com/paper/2403.04121/) (2024)
- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots](https://mlsanity.com/paper/2404.04066/) (2024)
- [Leveraging Multimodal Haptic Sensory Data for Robust Cutting](https://mlsanity.com/paper/1909.12460/) (2019)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items](https://mlsanity.com/paper/1908.07088/) (2019)
- [The STRANDS Project: Long-Term Autonomy in Everyday Environments](https://mlsanity.com/paper/1604.04384/) (2016)
- [Learning Representations that Enable Generalization in Assistive Tasks](https://mlsanity.com/paper/2212.03175/) (2022)
- [Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding](https://mlsanity.com/paper/2011.02604/) (2020)
- [LAVA: Long-horizon Visual Action based Food Acquisition](https://mlsanity.com/paper/2403.12876/) (2024)
- [FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes](https://mlsanity.com/paper/2407.07561/) (2024)
- [Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control](https://mlsanity.com/paper/2403.04067/) (2024)

## Built on by (most cited first, 7 of 7)

- [MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied Agents](https://mlsanity.com/paper/2511.23055/) (2025)
- [Vision Language Models Cannot Plan, but Can They Formalize?](https://mlsanity.com/paper/2509.21576/) (2025)
- [Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation](https://mlsanity.com/paper/2511.19647/) (2025)
- [CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog](https://mlsanity.com/paper/2605.19501/) (2026)
- [QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents](https://mlsanity.com/paper/2511.17855/) (2025)
- [Robot-Assisted Social Dining as a White Glove Service](https://mlsanity.com/paper/2602.15767/) (2026)
- [OmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interaction](https://mlsanity.com/paper/2604.28197/) (2026)

## Abstract (arXiv)

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