An Adaptable, Safe, and Portable Robot-Assisted Feeding System
Gordon, Ethan Kroll, Jenamani, Rajat Kumar, Nanavati, Amal, Liu, Ziang, Bolotski, Haya, Karim, Raida, Stabile, Daniel, Kashyap, Atharva, Zhu, Bernie Hao, Dai, Xilai, Schrenk, Tyler, Ko, Jonathan, Faulkner, Taylor Kessler, Bhattacharjee, Tapomayukh, Srinivasa, Siddhartha
–arXiv.org Artificial Intelligence
We demonstrate a robot-assisted feeding system that enables people with mobility impairments to feed themselves. Our system design embodies Safety, Portability, and User Control, with comprehensive full-stack safety checks, the ability to be mounted on and powered by any powered wheelchair, and a custom web-app allowing care-recipients to leverage their own assistive devices for robot control. For bite acquisition, we leverage multi-modal online learning to tractably adapt to unseen food types. For bite transfer, we leverage real-time mouth perception and interaction-aware control. Co-designed with community researchers, our system has been validated through multiple end-user studies.
arXiv.org Artificial Intelligence
Mar-6-2024
- Country:
- North America > United States
- New York (0.30)
- Washington > King County
- Seattle (0.16)
- North America > United States
- Genre:
- Research Report (0.50)
- Industry:
- Health & Medicine > Therapeutic Area (0.69)
- Technology:
- Information Technology > Artificial Intelligence > Robots (1.00)