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

A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes