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Grasping is among the most fundamental and long-lasting problems in roboticsstudy. This paper studies the problem of 6-DoF(degree of freedom) grasping by aparallel gripper in a cluttered scene captured using a commodity depth sensorfrom a single viewpoint. We address the problem in a learning-based framework.At the high level, we rely on a single-shot grasp proposal network, trainedwith synthetic data and tested in real-world scenarios. Our single-shot neuralnetwork architecture can predict amodal grasp proposal efficiently andeffectively. Our training data synthesis pipeline can generate scenes ofcomplex object configuration and leverage an innovative gripper contact modelto create dense and high-quality grasp annotations. Experiments in syntheticand real environments have demonstrated that the proposed approach canoutperform state-of-the-arts by a large margin.

S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes