Existing grasp synthesis methods are either analytical or data-driven. The former one is oftentimes limited to specific application scope. The latter one depends heavily on demonstrations, thus suffers from generalization issues; \eg, models trained with human grasp data would be difficult to transfer to 3-finger grippers. To tackle these deficiencies, we formulate a fast and differentiable force closure estimation method, capable of producing diverse and physically stable grasps with arbitrary hand structures, without any training data. Although force closure has commonly served as a measure of grasp quality, it has not been widely adopted as an optimization objective for grasp synthesis primarily due to its high computational complexity; in comparison, the proposed differentiable method can test a force closure within milliseconds. In experiments, we validate the proposed method's efficacy in 6 different settings.

翻译：现有的掌握式综合方法要么是分析性的,要么是数据驱动的,前者通常限于特定应用范围,后者主要依赖示范,因此有普遍性问题;因此,经人掌握式数据培训的模式很难转让给三指控制器。为了解决这些缺陷,我们制定了一种快速和可区分的武力封闭估计方法,能够在没有任何培训数据的情况下,用任意的手结构产生多样和物理稳定的控制器。虽然关闭武力通常是一种掌握式质量的衡量标准,但主要由于计算复杂程度高,它并没有被广泛作为掌握综合的优化目标;相比之下,拟议的不同方法可以在毫秒内测试武力封闭。在实验中,我们验证了在6个不同环境中拟议方法的功效。