Many approaches to grasp synthesis optimize analytic quality metrics that measure grasp robustness based on finger placements and local surface geometry. However, generating feasible dexterous grasps by optimizing these metrics is slow, often taking minutes. To address this issue, this paper presents FRoGGeR: a method that quickly generates robust precision grasps using the min-weight metric, a novel, almost-everywhere differentiable approximation of the classical epsilon grasp metric. The min-weight metric is simple and interpretable, provides a reasonable measure of grasp robustness, and admits numerically efficient gradients for smooth optimization. We leverage these properties to rapidly synthesize collision-free robust grasps - typically in less than a second. FRoGGeR can refine the candidate grasps generated by other methods (heuristic, data-driven, etc.) and is compatible with many object representations (SDFs, meshes, etc.). We study FRoGGeR's performance on over 40 objects drawn from the YCB dataset, outperforming a competitive baseline in computation time, feasibility rate of grasp synthesis, and picking success in simulation. We conclude that FRoGGeR is fast: it has a median synthesis time of 0.834s over hundreds of experiments.

翻译：许多抓取合成方法通过基于手指位置和局部表面几何形状优化分析性质量度量，来评估抓取鲁棒性。然而，通过优化这些度量生成可行的灵巧抓取通常耗时数分钟。为解决这一问题，本文提出FRoGGeR：一种利用最小权重度量（即经典ε抓取度量的新型几乎处处可微近似）快速生成鲁棒精准抓取的方法。该度量简洁且可解释，能提供合理的抓取鲁棒性评估，并支持平滑优化的数值高效梯度。我们利用这些特性快速合成无碰撞鲁棒抓取——通常亚秒级完成。FRoGGeR可优化由其他方法（启发式、数据驱动等）生成的候选抓取，并兼容多种物体表示（符号距离函数、网格等）。我们基于YCB数据集的40余个物体对FRoGGeR进行性能评估，其在计算时间、抓取合成可行性率及仿真拾取成功率方面均优于竞争基线。结论表明，FRoGGeR具有快速性：在数百次实验中，其中位合成时间为0.834秒。