Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
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The objective of the proposed thesis is to illustrate the idea of using synthetic data and deep networks to recognize primitive shapes for facilitating object grasping. 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 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 96% success rate. For task-oriented grasping, it achieves an 82% success rate. Overall, the method supports the hypothesis that shape primitives can support task-free and task-relevant grasp prediction.