  We approach the problem of high-DOF reaching-and-grasping via learning joint
planning of grasp and motion with deep reinforcement learning. To resolve the
sample efficiency issue in learning the high-dimensional and complex control of
dexterous grasping, we propose an effective representation of grasping state
characterizing the spatial interaction between the gripper and the target
object. To represent gripper-object interaction, we adopt Interaction Bisector
Surface (IBS) which is the Voronoi diagram between two close by 3D geometric
objects and has been successfully applied in characterizing spatial relations
between 3D objects. We found that IBS is surprisingly effective as a state
representation since it well informs the fine-grained control of each finger
with spatial relation against the target object. This novel grasp
representation, together with several technical contributions including a fast
IBS approximation, a novel vector-based reward and an effective training
strategy, facilitate learning a strong control model of high-DOF grasping with
good sample efficiency, dynamic adaptability, and cross-category generality.
Experiments show that it generates high-quality dexterous grasp for complex
shapes with smooth grasping motions.
