  Learning robotic grasps from visual observations is a promising yet
challenging task. Recent research shows its great potential by preparing and
learning from large-scale synthetic datasets. For the popular, 6
degree-of-freedom (6-DOF) grasp setting of parallel-jaw gripper, most of
existing methods take the strategy of heuristically sampling grasp candidates
and then evaluating them using learned scoring functions. This strategy is
limited in terms of the conflict between sampling efficiency and coverage of
optimal 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 grasps
for an unseen object observed from a single and unknown camera view. GPNet
builds on a key design of grasp proposal module that defines \emph{anchors of
grasp centers} at discrete but regular 3D grid corners, which is flexible to
support either more precise or more diverse grasp predictions. To test GPNet,
we contribute a synthetic dataset of 6-DOF object grasps; evaluation is
conducted 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 dataset
publicly available.
