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Learning robotic grasps from visual observations is a promising yetchallenging task. Recent research shows its great potential by preparing andlearning from large-scale synthetic datasets. For the popular, 6degree-of-freedom (6-DOF) grasp setting of parallel-jaw gripper, most ofexisting methods take the strategy of heuristically sampling grasp candidatesand then evaluating them using learned scoring functions. This strategy islimited in terms of the conflict between sampling efficiency and coverage ofoptimal grasps. To this end, we propose in this work a novel, end-to-end\emph{Grasp Proposal Network (GPNet)}, to predict a diverse set of 6-DOF graspsfor an unseen object observed from a single and unknown camera view. GPNetbuilds on a key design of grasp proposal module that defines \emph{anchors ofgrasp centers} at discrete but regular 3D grid corners, which is flexible tosupport either more precise or more diverse grasp predictions. To test GPNet,we contribute a synthetic dataset of 6-DOF object grasps; evaluation isconducted using rule-based criteria, simulation test, and real test.Comparative results show the advantage of our methods over existing ones.Notably, GPNet gains better simulation results via the specified coverage,which helps achieve a ready translation in real test. We will make our datasetpublicly available.

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps