  A segmentation-based architecture is proposed to decompose objects into
multiple primitive shapes from monocular depth input for robotic manipulation.
The backbone deep network is trained on synthetic data with 6 classes of
primitive shapes generated by a simulation engine. Each primitive shape is
designed with parametrized grasp families, permitting the pipeline to identify
multiple grasp candidates per shape primitive region. The grasps are priority
ordered via proposed ranking algorithm, with the first feasible one chosen for
execution. On task-free grasping of individual objects, the method achieves a
94% success rate. On task-oriented grasping, it achieves a 76% success rate.
Overall, the method supports the hypothesis that shape primitives can support
task-free and task-relevant grasp prediction.
