  Object manipulation in cluttered scenes is a difficult and important problem
in robotics. To efficiently manipulate objects, it is crucial to understand
their surroundings, especially in cases where multiple objects are stacked one
on top of the other, preventing effective grasping. We here present DUQIM-Net,
a decision-making approach for object manipulation in a setting of stacked
objects. In DUQIM-Net, the hierarchical stacking relationship is assessed using
Adj-Net, a model that leverages existing Transformer Encoder-Decoder object
detectors by adding an adjacency head. The output of this head
probabilistically infers the underlying hierarchical structure of the objects
in the scene. We utilize the properties of the adjacency matrix in DUQIM-Net to
perform decision making and assist with object-grasping tasks. Our experimental
results show that Adj-Net surpasses the state-of-the-art in object-relationship
inference on the Visual Manipulation Relationship Dataset (VMRD), and that
DUQIM-Net outperforms comparable approaches in bin clearing tasks.
