  Autonomous robotic grasping plays an important role in intelligent robotics.
However, how to help the robot grasp specific objects in object stacking scenes
is still an open problem, because there are two main challenges for autonomous
robots: (1)it is a comprehensive task to know what and how to grasp; (2)it is
hard to deal with the situations in which the target is hidden or covered by
other objects. In this paper, we propose a multi-task convolutional neural
network for autonomous robotic grasping, which can help the robot find the
target, make the plan for grasping and finally grasp the target step by step in
object stacking scenes. We integrate vision-based robotic grasping detection
and visual manipulation relationship reasoning in one single deep network and
build the autonomous robotic grasping system. Experimental results demonstrate
that with our model, Baxter robot can autonomously grasp the target with a
success rate of 90.6%, 71.9% and 59.4% in object cluttered scenes, familiar
stacking scenes and complex stacking scenes respectively.
