  Shape informs how an object should be grasped, both in terms of where and
how. As such, this paper describes a segmentation-based architecture for
decomposing objects sensed with a depth camera into multiple primitive shapes,
along with a post-processing pipeline for robotic grasping. Segmentation
employs a deep network, called PS-CNN, trained on synthetic data with 6 classes
of primitive shapes and generated using a simulation engine. Each primitive
shape is designed with parametrized grasp families, permitting the pipeline to
identify multiple grasp candidates per shape region. The grasps are rank
ordered, with the first feasible one chosen for execution. For task-free
grasping of individual objects, the method achieves a 94.2% success rate
placing it amongst the top performing grasp methods when compared to top-down
and SE(3)-based approaches. Additional tests involving variable viewpoints and
clutter demonstrate robustness to setup. For task-oriented grasping, PS-CNN
achieves a 93.0% success rate. Overall, the outcomes support the hypothesis
that explicitly encoding shape primitives within a grasping pipeline should
boost grasping performance, including task-free and task-relevant grasp
prediction.
