  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.
