  Efficient and robust grasp pose detection is vital for robotic manipulation.
For general 6 DoF grasping, conventional methods treat all points in a scene
equally and usually adopt uniform sampling to select grasp candidates. However,
we discover that ignoring where to grasp greatly harms the speed and accuracy
of current grasp pose detection methods. In this paper, we propose "graspness",
a quality based on geometry cues that distinguishes graspable areas in
cluttered scenes. A look-ahead searching method is proposed for measuring the
graspness and statistical results justify the rationality of our method. To
quickly detect graspness in practice, we develop a neural network named
cascaded graspness model to approximate the searching process. Extensive
experiments verify the stability, generality and effectiveness of our graspness
model, allowing it to be used as a plug-and-play module for different methods.
A large improvement in accuracy is witnessed for various previous methods after
equipping our graspness model. Moreover, we develop GSNet, an end-to-end
network that incorporates our graspness model for early filtering of
low-quality predictions. Experiments on a large-scale benchmark,
GraspNet-1Billion, show that our method outperforms previous arts by a large
margin (30+ AP) and achieves a high inference speed. The library of GSNet has
been integrated into AnyGrasp, which is at
https://github.com/graspnet/anygrasp_sdk.
