  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. As a result, we show the Ferrari-Canny metric -- which measures
the size of external disturbances a grasp can reject -- bounds the friction
cone uncertainty a grasp can tolerate, and thus also measures intrinsic
robustness. In tandem, we show that the recently proposed min-weight metric
lower bounds the Ferrari-Canny metric, justifying it as a
computationally-efficient, uncertainty-aware alternative. We validate this
theory on hardware experiments versus a competitive baseline and demonstrate
superior performance. Finally, we use our theory to develop an analytic notion
of probabilistic force closure, which we show in simulation generates grasps
that can incorporate uncertainty distributions over an object's geometry.
