# Robot-Assisted Social Dining as a White Glove Service

arXiv: https://arxiv.org/abs/2602.15767 · HTML: https://mlsanity.com/paper/2602.15767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 84 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022)
- [Detecting and Preventing Hallucinations in Large Vision Language Models](https://mlsanity.com/paper/2308.06394/) (2023)
- [Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models](https://mlsanity.com/paper/2309.04316/) (2023)
- [Towards Robotic Feeding: Role of Haptics in Fork-based Food Manipulation](https://mlsanity.com/paper/1804.08768/) (2018)
- [VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots](https://mlsanity.com/paper/2404.04066/) (2024)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [Blox-Net: Generative Design-for-Robot-Assembly Using VLM Supervision, Physics Simulation, and a Robot with Reset](https://mlsanity.com/paper/2409.17126/) (2024)
- [Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items](https://mlsanity.com/paper/1908.07088/) (2019)
- [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)
- [Balancing Efficiency and Comfort in Robot-Assisted Bite Transfer](https://mlsanity.com/paper/2111.11401/) (2021)
- [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)
- [In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing](https://mlsanity.com/paper/2211.12705/) (2022)
- [Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups](https://mlsanity.com/paper/2207.03348/) (2022)
- [Robot-Assisted Feeding: Generalizing Skewering Strategies across Food Items on a Realistic Plate](https://mlsanity.com/paper/1906.02350/) (2019)
- [An Adaptable, Safe, and Portable Robot-Assisted Feeding System](https://mlsanity.com/paper/2403.04134/) (2024)
- [Kiri-Spoon: A Soft Shape-Changing Utensil for Robot-Assisted Feeding](https://mlsanity.com/paper/2403.05784/) (2024)
- [Augmented Body Communicator: Enhancing daily body expression for people with upper limb limitations through LLM and a robotic arm](https://mlsanity.com/paper/2505.05832/) (2025)
- [Mechanical Design Improvement of a Passive Device to Assist Eating in People Living with Movement Disorders](https://mlsanity.com/paper/2010.06752/) (2020)
- [Kiri-Spoon: A Kirigami Utensil for Robot-Assisted Feeding](https://mlsanity.com/paper/2501.01323/) (2025)

## Abstract (arXiv)

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.