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.

翻译：生成任意物体上的稳定鲁棒抓取是灵巧机器人手的关键能力，标志着向高级灵巧操作迈出的重要一步。以往研究大多聚焦于改进可微抓取度量指标，其前提假设是对物体几何形状具有精确认知。然而，由于噪声和部分形状观测的存在，形状不确定性广泛存在，给抓取规划带来了挑战。我们提出SpringGrasp规划器，该方法考虑物体表面的不确定性观测来合成柔顺灵巧抓取。柔顺灵巧抓取能最小化与物体意外接触的影响，从而在形状不确定物体上实现更稳定的抓取。我们引入了一个解析可微的度量指标——SpringGrasp度量，用于评估整个柔顺抓取过程的动态行为。通过SpringGrasp规划器进行规划，在包含14种常见物体的真实机器人实验中，我们的方法从两个视角实现了89%的抓取成功率，从单一视角实现了84%的成功率。与基于力闭合的规划器相比，我们的方法抓取成功率至少提高了18%。