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In this work, we introduce a novel, end-to-end trainable CNN-basedarchitecture to deliver high quality results for grasp detection suitable for aparallel-plate gripper, and semantic segmentation. Utilizing this, we propose anovel refinement module that takes advantage of previously calculated graspdetection and semantic segmentation and further increases grasp detectionaccuracy. Our proposed network delivers state-of-the-art accuracy on twopopular grasp dataset, namely Cornell and Jacquard. As additional contribution,we provide a novel dataset extension for the OCID dataset, making it possibleto evaluate grasp detection in highly challenging scenes. Using this dataset,we show that semantic segmentation can additionally be used to assign graspcandidates to object classes, which can be used to pick specific objects in thescene.

End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB