A Deep Learning-based Grasp Pose Estimation Approach for Large-Size Deformable Objects in Clutter

Deformable objects especially large-size deformable objects grasping is unappreciated but widespread in industrial applications (e.g., clothes recycling). While it encounters several challenges, for example, the existing methods didn't take large-size deformable objects into account, no typical boundary of deformable objects. To solve the challenges, we proposed a grasp detection framework consisting of a self-trained object detection network, an instance segmentation module, and a grasp pose generation pipeline. The experiments were successfully conducted on the industrial laundry mockup with an 88.9% success ratio. The experiments result indicates the effectiveness of the proposed framework on spatial-constrained large-size deformable objects grasping in clutter.