  Suction is an important solution for the longstanding robotic grasping
problem. Compared with other kinds of grasping, suction grasping is easier to
represent and often more reliable in practice. Though preferred in many
scenarios, it is not fully investigated and lacks sufficient training data and
evaluation benchmarks. To address that, firstly, we propose a new physical
model to analytically evaluate seal formation and wrench resistance of a
suction grasping, which are two key aspects of grasp success. Secondly, a
two-step methodology is adopted to generate annotations on a large-scale
dataset collected in real-world cluttered scenarios. Thirdly, a standard online
evaluation system is proposed to evaluate suction poses in continuous operation
space, which can benchmark different algorithms fairly without the need of
exhaustive labeling. Real-robot experiments are conducted to show that our
annotations align well with real world. Meanwhile, we propose a method to
predict numerous suction poses from an RGB-D image of a cluttered scene and
demonstrate our superiority against several previous methods. Result analyses
are further provided to help readers better understand the challenges in this
area. Data and source code are publicly available at www.graspnet.net.
