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.
@article{arxiv.2305.07631,
title = {Vision and Control for Grasping Clear Plastic Bags},
author = {Joohwan Seo and Jackson Wagner and Anuj Raicura and Jake Kim},
journal= {arXiv preprint arXiv:2305.07631},
year = {2023}
}