  We consider the problem of closed-loop robotic grasping and present a novel
planner which uses Visual Feedback and an uncertainty-aware Adaptive Sampling
strategy (VFAS) to close the loop. At each iteration, our method VFAS-Grasp
builds a set of candidate grasps by generating random perturbations of a seed
grasp. The candidates are then scored using a novel metric which combines a
learned grasp-quality estimator, the uncertainty in the estimate and the
distance from the seed proposal to promote temporal consistency. Additionally,
we present two mechanisms to improve the efficiency of our sampling strategy:
We dynamically scale the sampling region size and number of samples in it based
on past grasp scores. We also leverage a motion vector field estimator to shift
the center of our sampling region. We demonstrate that our algorithm can run in
real time (20 Hz) and is capable of improving grasp performance for static
scenes by refining the initial grasp proposal. We also show that it can enable
grasping of slow moving objects, such as those encountered during human to
robot handover.
