  Aiming at the traditional grasping method for manipulators based on 2D
camera, when faced with the scene of gathering or covering, it can hardly
perform well in unstructured scenes that appear as gathering and covering, for
the reason that can't recognize objects accurately in cluster scenes from a
single perspective and the manipulators can't make the environment better for
grasping. In this case, a novel method of pushing-grasping collaborative based
on the deep Q-network in dual perspectives is proposed in this paper. This
method adopts an improved deep Q network algorithm, with an RGB-D camera to
obtain the information of objects' RGB images and point clouds from two
perspectives, and combines the pushing and grasping actions so that the trained
manipulator can make the scenes better for grasping so that it can perform well
in more complicated grasping scenes. What's more, we improved the reward
function of the deep Q-network and propose the piecewise reward function to
speed up the convergence of the deep Q-network. We trained different models and
tried different methods in the V-REP simulation environment, and it concluded
that the method proposed in this paper converges quickly and the success rate
of grasping objects in unstructured scenes raises up to 83.5%. Besides, it
shows the generalization ability and well performance when novel objects appear
in the scenes that the manipulator has never grasped before.
