SpringGrasp：在形状不确定性下合成顺应性灵巧抓取
SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出SpringGrasp规划器与指标，在形状不确定性下合成顺应性灵巧抓取以最小化意外接触影响，在真实机器人实验中较基于力闭合的规划器将抓取成功率至少提升18%。
AI中文摘要：
为任意物体生成稳定且鲁棒的抓取对于灵巧机械手至关重要，标志着向高级灵巧操作迈出的重要一步。以往的研究大多致力于改进可微抓取指标，并假设物体几何形状是精确已知的。然而，由于存在噪声和部分形状观测，形状不确定性无处不在，这给抓取规划带来了挑战。我们提出了SpringGrasp规划器，该规划器考虑了物体表面的不确定性观测，用于合成顺应性灵巧抓取。顺应性灵巧抓取能够最小化与物体意外接触的影响，从而在形状不确定的物体上实现更稳定的抓取。我们引入了一种分析性且可微的指标——SpringGrasp指标，用于评估整个顺应性抓取过程的动态行为。在真实机器人对14个常见物体的实验中，使用SpringGrasp规划器进行规划，我们的方法在两个视角下实现了89%的抓取成功率，在单个视角下实现了84%的抓取成功率。与基于力闭合的规划器相比，我们的方法的抓取成功率至少提高了18%。
英文摘要：
Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studies have mostly focused on improving differentiable grasping metrics with the assumption of precisely known object geometry. However, shape uncertainty is ubiquitous due to noisy and partial shape observations, which introduce challenges in grasp planning. We propose, SpringGrasp planner, a planner that considers uncertain observations of the object surface for synthesizing compliant dexterous grasps. A compliant dexterous grasp could minimize the effect of unexpected contact with the object, leading to more stable grasp with shape-uncertain objects. We introduce an analytical and differentiable metric, SpringGrasp metric, that evaluates the dynamic behavior of the entire compliant grasping process. Planning with SpringGrasp planner, our method achieves a grasp success rate of 89% from two viewpoints and 84% from a single viewpoints in experiment with a real robot on 14 common objects. Compared with a force-closure based planner, our method achieves at least 18% higher grasp success rate.
