迈向灵巧抓取内在鲁棒性的解析理论
Toward An Analytic Theory of Intrinsic Robustness for Dexterous Grasping
AI总结：
本文提出一种抓取解析理论，通过刻画摩擦锥不确定性对力闭合状态的影响来研究执行前抓取规划的内在鲁棒性，并据此分析了两种抓取指标的理论保证，同时开发了概率力闭合概念以生成感知不确定性的抓取。
摘要：
传统的抓取规划方法要求完全掌握物体的位姿和几何信息。这些量的不确定性会导致规划抓取质量的不确定性，进而可能引发失败。经典意义上，抓取鲁棒性是指在抓取物体后抵抗外部扰动的能力。相比之下，本研究探讨的是针对影响执行前抓取规划的内在不确定性源（如物体位姿或几何形状）的鲁棒性。为此，我们提出了一种新颖的抓取解析理论，该理论通过刻画摩擦锥不确定性对抓取力闭合状态的影响，来推理这种内在鲁棒性。我们从两个方面应用了该成果。首先，我们分析了文献中两种抓取指标（经典的Ferrari-Canny指标和较新的min-weight指标）在内在鲁棒性上的理论保证。我们通过硬件试验验证了这些结果，比较了有无鲁棒性保证所合成的抓取，显示出成功率的显著提升。其次，我们利用该理论发展出一种新颖的解析概率力闭合概念，并在仿真中证明其能够生成独特的、感知不确定性的抓取。
英文摘要：
Conventional approaches to grasp planning require perfect knowledge of an object's pose and geometry. Uncertainties in these quantities induce uncertainties in the quality of planned grasps, which can lead to failure. Classically, grasp robustness refers to the ability to resist external disturbances after grasping an object. In contrast, this work studies robustness to intrinsic sources of uncertainty like object pose or geometry affecting grasp planning before execution. To do so, we develop a novel analytic theory of grasping that reasons about this intrinsic robustness by characterizing the effect of friction cone uncertainty on a grasp's force closure status. We apply this result in two ways. First, we analyze the theoretical guarantees on intrinsic robustness of two grasp metrics in the literature, the classical Ferrari-Canny metric and more recent min-weight metric. We validate these results with hardware trials that compare grasps synthesized with and without robustness guarantees, showing a clear improvement in success rates. Second, we use our theory to develop a novel analytic notion of probabilistic force closure, which we show can generate unique, uncertainty-aware grasps in simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
