用仿真预处理提升机器人夹取软食成功率，平均提高27%。
REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding
- 先在仿真中试推、切、翻等动作，再实测验证
- 15个餐盘10类食物测试，平均成功率提升27%
- 适合做康复辅助机器人或智能餐具研发者
本文提出REPeat，一种用于机器人辅助进食中软质食物夹取的Real2Sim2Real框架。通过预夹取动作（如推动、切割、翻转）改善直接夹取（如穿刺、舀取、缠绕）的成功率。当数据驱动模型预测直接夹取成功率低时，系统进入真实→仿真阶段，重建食物几何并模拟多种预处理动作；随后通过仿真生成逼真图像进行成功评估（Sim2Real）。若评估结果改善，则在现实中执行该动作。在包含10种软食的15个不同餐盘上测试，平均夹取成功率提升27%。
原文摘要 · Abstract (English)
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.
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