  Robotic dexterous grasping is a challenging problem due to the high degree of
freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing
deep reinforcement learning (DRL) based methods leverage human demonstrations
to reduce sample complexity due to the high dimensional action space with
dexterous grasping. However, less attention has been paid to hand-object
interaction representations for high-level generalization. In this paper, we
propose a novel geometric and spatial hand-object interaction representation,
named DexRep, to capture dynamic object shape features and the spatial
relations between hands and objects during grasping. DexRep comprises Occupancy
Feature for rough shapes within sensing range by moving hands, Surface Feature
for changing hand-object surface distances, and Local-Geo Feature for local
geometric surface features most related to potential contacts. Based on the new
representation, we propose a dexterous deep reinforcement learning method to
learn a generalizable grasping policy DexRepNet. Experimental results show that
our method outperforms baselines using existing representations for robotic
grasping dramatically both in grasp success rate and convergence speed. It
achieves a 93% grasping success rate on seen objects and higher than 80%
grasping success rates on diverse objects of unseen categories in both
simulation and real-world experiments.
