  Recent advances in multi-fingered robotic grasping have enabled fast
6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in
cluttered scenes, on the other hand, remains mostly unexplored due to the added
difficulty of reasoning over obstacles which greatly increases the
computational time to generate high-quality collision-free grasps. In this work
we address such limitations by introducing DDGC, a fast generative multi-finger
grasp sampling method that can generate high quality grasps in cluttered scenes
from a single RGB-D image. DDGC is built as a network that encodes scene
information to produce coarse-to-fine collision-free grasp poses and
configurations. We experimentally benchmark DDGC against the
simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and
7 real world scenes. The results show that DDGC outperforms the baseline on
synthesizing high-quality grasps and removing clutter while being 5 times
faster. This, in turn, opens up the door for using multi-finger grasps in
practical applications which has so far been limited due to the excessive
computation time needed by other methods.
