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的分析质量度量来优化抓取鲁棒性。然而，通过优化这些度量生成可行的灵巧抓取速度缓慢，通常需要数分钟。为解决该问题，本文提出FRoGGeR：一种利用最小权重度量快速生成鲁棒精密抓取的方法，该度量是经典ε抓取度量的新颖、几乎处处可微的近似。最小权重度量简单且可解释，能提供合理的抓取鲁棒性度量，并支持用于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的鲁棒抓取——通常在一秒内完成。FRoGGeR可优化其他方法（启发式、数据驱动等）生成的候选抓取，并与多种物体表示（SDF、网格等）兼容。我们在YCB数据集的40多个物体上研究FRoGGeR的性能，在计算时间、抓取合成可行性率和仿真拾取成功率方面均优于竞争基线。实验表明FRoGGeR速度极快：在数百次实验中，其中位合成时间为0.834秒。