REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding
Year: 2024
Type: conference-paper
Abstract: The paper presents REPeat, a Real2Sim2Real framework designed to enhance bite acquisition in robot-assisted feeding for soft foods. It uses ‘pre-acquisition actions’ such as pushing, cutting, and flipping to improve the success rate of bite acquisition actions such as skewering, scooping, and twirling. If the data-driven model predicts low success for direct bite acquisition, the system initiates a Real2Sim phase, reconstructing the food’s geometry in a simulation. The robot explores various pre-acquisition actions in the simulation, then a Sim2Real step renders a photorealistic image to reassess success rates. If the success improves, the robot applies the action in reality. We evaluate the system on 15 diverse plates with 10 types of food items for a soft food diet, showing improvement in bite acquisition success rates by 27% on average across all plates. See our project website at emprise.cs.cornell.edu/repeat.
Authors: Nayoung Ha, Ruolin Ye, Ziang Liu, Shubhangi Sinha, Tapomayukh Bhattacharjee
Institution: Cornell University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4405778602