  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;
e.g., 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 4ms. In
experiments, we validate the proposed method's efficacy in 8 different
settings.
