  Robotic manipulation tasks, such as object rearrangement, play a crucial role
in enabling robots to interact with complex and arbitrary environments.
Existing work focuses primarily on single-level rearrangement planning and,
even if multiple levels exist, dependency relations among substructures are
geometrically simpler, like tower stacking. We propose Structural Concept
Learning (SCL), a deep learning approach that leverages graph attention
networks to perform multi-level object rearrangement planning for scenes with
structural dependency hierarchies. It is trained on a self-generated simulation
data set with intuitive structures, works for unseen scenes with an arbitrary
number of objects and higher complexity of structures, infers independent
substructures to allow for task parallelization over multiple manipulators, and
generalizes to the real world. We compare our method with a range of classical
and model-based baselines to show that our method leverages its scene
understanding to achieve better performance, flexibility, and efficiency. The
dataset, supplementary details, videos, and code implementation are available
at: https://manavkulshrestha.github.io/scl
