HEURISTIC-BASED ROBOTIC GRASPS

In some cases, images and depth maps can define bins with objects in random configurations. It is recognized herein that current approaches to training deep neural networks to perform grasp computations lack capabilities and efficiencies, such that the resulting grasp computations and grasps can be imprecise or cumbersome, among other shortcomings. Synthetic depth images can be labeled with grasp annotations that are generated based on heuristic-based analyses, so as to define annotated synthetic datasets. The annotated synthetic datasets can be used to train neural networks to determine the best grasp locations for different objects arranged in a variety of positions with respect to each other.