  6-DoF object-agnostic grasping in unstructured environments is a critical yet
challenging task in robotics. Most current works use non-optimized approaches
to sample grasp locations and learn spatial features without concerning the
grasping task. This paper proposes GraNet, a graph-based grasp pose generation
framework that translates a point cloud scene into multi-level graphs and
propagates features through graph neural networks. By building graphs at the
scene level, object level, and grasp point level, GraNet enhances feature
embedding at multiple scales while progressively converging to the ideal
grasping locations by learning. Our pipeline can thus characterize the spatial
distribution of grasps in cluttered scenes, leading to a higher rate of
effective grasping. Furthermore, we enhance the representation ability of
scalable graph networks by a structure-aware attention mechanism to exploit
local relations in graphs. Our method achieves state-of-the-art performance on
the large-scale GraspNet-1Billion benchmark, especially in grasping unseen
objects (+11.62 AP). The real robot experiment shows a high success rate in
grasping scattered objects, verifying the effectiveness of the proposed
approach in unstructured environments.
