Lessons Learned from Designing and Evaluating a Robot-assisted Feeding System for Out-of-lab Use

Abstract

Millions of people cannot eat independently due to a disability, and caregiver-assisted meals can make them feel self-conscious, pressured, or burdensome. Robot-assisted feeding promises to empower people with motor impairments to feed themselves. However, current research typically examines specifc robotic system subcomponents and evaluates them in controlled lab settings. This leaves a gap in developing and evaluating an end-to-end system that can feed entire meals in out-of-lab settings. We present one such system, which we developed collaboratively with two community researchers (CRs) with motor-impairments. The key challenge of developing a robot feeding system for out-of-lab use is the varied off-nominal scenarios that inevitably arise. Our key insight is that users can overcome many off-nominals, provided customizability and control over the system. Our system improves upon the state-ofthe-art with: (1) a user interface that provides substantial user customizability and control, (2) a bite selection implementation that incorporates users-in-the-loop to generalize across food items, and (3) portable hardware that facilitates system use in diverse environments without inhibiting user mobility. We conduct two studies to evaluate the system. In Study 1, fve users with motor impairments and one CR use the system to feed themselves meals of their choice in a cafeteria, offce, or conference room. In Study 2, one CR uses the system in his home for fve days, feeding himself 10 meals across diverse contexts. We present 3 key lesson learned: (1) spatial contexts are numerous, customizability lets users adapt to them; (2) offnominals will arise, variable autonomy lets users overcome them; and (3) assistive robots’ benefts depend on context.

BibTeX Entry

@inproceedings{nanavati2025lessons,
  title = {Lessons Learned from Designing and Evaluating a Robot-assisted Feeding System for Out-of-lab Use},
  author = {Nanavati, Amal and Gordon, Ethan K and Kessler Faulkner, Taylor A and Song, Yuxin (Ray) and Ko, Johnathan and Schrenk, Tyler and Nguyen, Vy and Zhu, Bernie Hao and Bolotski, Haya and Kashyap, Atharva and Kutty, Sriram and Karim, Raida and Rainbolt, Liander and Scalise, Rosario and Song, Hanjun and Qu, Ramon and Cakmak, Maya and Srinivasa, Siddhartha S},
  year = {2025},
  month = mar,
  booktitle = {ACM/IEEE International Conference on Human-Robot Interaction (HRI)},
  type = {conference},
  video = {https://youtu.be/0GxLTw5UDt0?si=LKSq3WPBUSrR3m8F},
  url = {https://robotfeeding.io/publications/hri25a/}
}