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We consider the problem of closed-loop robotic grasping and present a novelplanner which uses Visual Feedback and an uncertainty-aware Adaptive Samplingstrategy (VFAS) to close the loop. At each iteration, our method VFAS-Graspbuilds a set of candidate grasps by generating random perturbations of a seedgrasp. The candidates are then scored using a novel metric which combines alearned grasp-quality estimator, the uncertainty in the estimate and thedistance 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 basedon past grasp scores. We also leverage a motion vector field estimator to shiftthe center of our sampling region. We demonstrate that our algorithm can run inreal time (20 Hz) and is capable of improving grasp performance for staticscenes by refining the initial grasp proposal. We also show that it can enablegrasping of slow moving objects, such as those encountered during human torobot handover.

VFAS-Grasp: Closed Loop Grasping with Visual Feedback and Adaptive Sampling