REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding
This addresses the problem of reliable feeding assistance for individuals with motor impairments, though it is incremental as it builds on existing robot-assisted feeding methods.
The paper tackles the challenge of improving bite acquisition success for soft foods in robot-assisted feeding by introducing a Real2Sim2Real framework that uses pre-acquisition actions like pushing and cutting, resulting in an average 27% improvement in success rates across diverse plates.
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 https://emprise.cs.cornell.edu/repeat.