A Multitask Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes 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 multitask 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 visionbased 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.

Taskoriented Grasping in Object Stacking Scenes with CRFbased Semantic Model In taskoriented grasping, the robot is supposed to manipulate the objects in a taskcompatible manner, which is more important but more challenging than just stably grasping. However, most of existing works perform taskoriented grasping only in single object scenes. This greatly limits their practical application in real world scenes, in which there are usually multiple stacked objects with serious overlaps and occlusions. To perform taskoriented grasping in object stacking scenes, in this paper, we firstly build a synthetic dataset named Object Stacking Grasping Dataset (OSGD) for taskoriented grasping in object stacking scenes. Secondly, a Conditional Random Field (CRF) is constructed to model the semantic contents in object regions. The modelled semantic contents can be illustrated as incompatibility of task labels and continuity of task regions. This proposed approach can greatly reduce the interference of overlaps and occlusions in object stacking scenes. To embed the CRFbased semantic model into our grasp detection network, we implement the inference process of CRFs as a RNN so that the whole model, Taskoriented Grasping CRFs (TOGCRFs) can be trained end to end. Finally, in object stacking scenes, the constructed model can help robot achieve 69.4% success rate for taskoriented grasping.