  Robots in the real world frequently come across identical objects in dense
clutter. When evaluating grasp poses in these scenarios, a target-driven
grasping system requires knowledge of spatial relations between scene objects
(e.g., proximity, adjacency, and occlusions). To efficiently complete this
task, we propose a target-driven grasping system that simultaneously considers
object relations and predicts 6-DoF grasp poses. A densely cluttered scene is
first formulated as a grasp graph with nodes representing object geometries in
the grasp coordinate frame and edges indicating spatial relations between the
objects. We design a Grasp Graph Neural Network (G2N2) that evaluates the grasp
graph and finds the most feasible 6-DoF grasp pose for a target object.
Additionally, we develop a shape completion-assisted grasp pose sampling method
that improves sample quality and consequently grasping efficiency. We compare
our method against several baselines in both simulated and real settings. In
real-world experiments with novel objects, our approach achieves a 77.78%
grasping accuracy in densely cluttered scenarios, surpassing the
best-performing baseline by more than 15%. Supplementary material is available
at https://sites.google.com/umn.edu/graph-grasping.
