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arXiv · 2305.07631

Vision and Control for Grasping Clear Plastic Bags

Joohwan Seo·Jackson Wagner·Anuj Raicura·Jake Kim
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Abstract

We develop two novel vision methods for planning effective grasps for clear plastic bags, as well as a control method to enable a Sawyer arm with a parallel gripper to execute the grasps. The first vision method is based on classical image processing and heuristics (e.g., Canny edge detection) to select a grasp target and angle. The second uses a deep-learning model trained on a human-labeled data set to mimic human grasp decisions. A clustering algorithm is used to de-noise the outputs of each vision method. Subsequently, a workspace PD control method is used to execute each grasp. Of the two vision methods, we find the deep-learning based method to be more effective.

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Joohwan Seo, Jackson Wagner, Anuj Raicura, Jake Kim. 2023-05-12. Vision and Control for Grasping Clear Plastic Bags. https://arxiv.org/abs/2305.07631

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