Robot grasping based on heuristics

Abstract

In some cases, the image and depth map can define bins with randomly configured objects. Among other drawbacks, the current approach of training a deep neural network to perform grasping calculations lacks in capabilities and efficiency, and it is understood that the resulting grasping calculations and grasps can be inaccurate or complex. The synthetic depth image can be labeled with grasping annotations generated based on heuristic-based analysis to define an annotated synthetic dataset. The annotated synthetic dataset can be used to train a neural network to determine the best grasping positions for various objects placed at various positions relative to each other.

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