# Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items

arXiv: https://arxiv.org/abs/1908.07088 · HTML: https://mlsanity.com/paper/1908.07088/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Learning Bimanual Scooping Policies for Food Acquisition](https://mlsanity.com/paper/2211.14652/) (2022)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes](https://mlsanity.com/paper/2407.07561/) (2024)
- [Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types](https://mlsanity.com/paper/2403.12891/) (2024)
- [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025)
- [Learning Sequential Acquisition Policies for Robot-Assisted Feeding](https://mlsanity.com/paper/2309.05197/) (2023)
- [IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition](https://mlsanity.com/paper/2409.12092/) (2024)
- [X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback](https://mlsanity.com/paper/2203.02072/) (2022)
- [REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding](https://mlsanity.com/paper/2410.10017/) (2024)
- ["Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration](https://mlsanity.com/paper/2310.18791/) (2023)
- [What AIs are not Learning (and Why)](https://mlsanity.com/paper/2404.04267/) (2024)
- [PrioriTouch: Adapting to User Contact Preferences for Whole-Arm Physical Human-Robot Interaction](https://mlsanity.com/paper/2509.18447/) (2025)
- [Robot-Assisted Social Dining as a White Glove Service](https://mlsanity.com/paper/2602.15767/) (2026)
- [VERAGMIL: Virtual Environment for Scooping Granular Foods with Imitation Learning Models](https://mlsanity.com/paper/2608.18258/) (2026)

## Abstract (arXiv)

A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items. It must adapt to changing user food preferences under uncertain visual and physical environments. Different food items in different environmental conditions require different manipulation strategies for successful bite acquisition. Therefore, a key challenge is how to handle previously unseen food items with very different success rate distributions over strategy. Combining low-level controllers and planners into discrete action trajectories, we show that the problem can be represented using a linear contextual bandit setting. We construct a simulated environment using a doubly robust loss estimate from previously seen food items, which we use to tune the parameters of off-the-shelf contextual bandit algorithms. Finally, we demonstrate empirically on a robot-assisted feeding system that, even starting with a model trained on thousands of skewering attempts on dissimilar previously seen food items, ε-greedy and LinUCB algorithms can quickly converge to the most successful manipulation strategy.