利用前一次抓取感知预测后续抓取力，提升机械手重抓效率。
Enhancing Regrasping Efficiency Using Prior Grasping Perceptions with Soft Fingertips
- 基于首次抓取的感知信息预测不同姿态下的抓取力
- 考虑指尖柔软性与物体不对称性，提升预测准确性
- 实验证明可显著提高多种日常物品的抓取效率
在处理工具或堆叠物品时，常需以不同姿态重复抓取同一物体。由于物体属性未知及抓取姿态变化，所需抓取力不确定且易变。传统方法依赖实时反馈谨慎控制抓取力，以防滑落或损坏，但忽略了首次抓取中可复用的信息，将每次重抓视为初次尝试，严重降低效率。为此，我们提出一种方法，利用前期抓取感知预测不同姿态下的所需抓取力，并引入考虑指尖软度和物体非对称性的计算模型。理论分析表明，单次抓取后即可实现跨姿态的抓取力预测。实验验证了该方法的准确性和适应性。结果还显示，将预测抓取力融入反馈控制策略，能显著提升多种日常物体的抓取效率。
原文摘要 · Abstract (English)
Grasping the same object in different postures is often necessary, especially when handling tools or stacked items. Due to unknown object properties and changes in grasping posture, the required grasping force is uncertain and variable. Traditional methods rely on real-time feedback to control the grasping force cautiously, aiming to prevent slipping or damage. However, they overlook reusable information from the initial grasp, treating subsequent regrasping attempts as if they were the first, which significantly reduces efficiency. To improve this, we propose a method that utilizes perception from prior grasping attempts to predict the required grasping force, even with changes in position. We also introduce a calculation method that accounts for fingertip softness and object asymmetry. Theoretical analyses demonstrate the feasibility of predicting grasping forces across various postures after a single grasp. Experimental verifications attest to the accuracy and adaptability of our prediction method. Furthermore, results show that incorporating the predicted grasping force into feedback-based approaches significantly enhances grasping efficiency across a range of everyday objects.
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