Cloth Region Segmentation for Robust Grasp Selection
This work addresses a specific challenge in robotic cloth manipulation for domestic and industrial tasks, but it is incremental in nature.
The paper tackles the problem of segmenting and grasping key regions (edges and corners) of cloth from depth images to improve robotic manipulation, demonstrating that their method outperforms baseline methods on grasping success.
Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many cloth-related tasks like laundry folding and bed making, it is crucial to manipulate specific regions like edges and corners, as opposed to folds. In this work, we focus on the problem of segmenting and grasping these key regions. Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds. We also provide a novel algorithm for estimating the grasp location, direction, and directional uncertainty from the segmentation. We demonstrate our method on a real robot system and show that it outperforms baseline methods on grasping success. Video and other supplementary materials are available at: https://sites.google.com/view/cloth-segmentation.