Center Direction Network for Grasping Point Localization on Cloths
This work addresses the challenge of robotic grasping for deformable objects like cloths, providing a robust solution and benchmark for researchers in computer vision and robotics, though it is incremental in advancing existing deep-learning approaches.
The paper tackles the problem of grasp point detection for deformable cloth objects in robotics and computer vision, introducing CeDiRNet-3DoF, which achieved first place in the ICRA 2023 Cloth Manipulation Challenge and outperformed state-of-the-art methods in real-world evaluations.
Object grasping is a fundamental challenge in robotics and computer vision, critical for advancing robotic manipulation capabilities. Deformable objects, like fabrics and cloths, pose additional challenges due to their non-rigid nature. In this work, we introduce CeDiRNet-3DoF, a deep-learning model for grasp point detection, with a particular focus on cloth objects. CeDiRNet-3DoF employs center direction regression alongside a localization network, attaining first place in the perception task of ICRA 2023's Cloth Manipulation Challenge. Recognizing the lack of standardized benchmarks in the literature that hinder effective method comparison, we present the ViCoS Towel Dataset. This extensive benchmark dataset comprises 8,000 real and 12,000 synthetic images, serving as a robust resource for training and evaluating contemporary data-driven deep-learning approaches. Extensive evaluation revealed CeDiRNet-3DoF's robustness in real-world performance, outperforming state-of-the-art methods, including the latest transformer-based models. Our work bridges a crucial gap, offering a robust solution and benchmark for cloth grasping in computer vision and robotics. Code and dataset are available at: https://github.com/vicoslab/CeDiRNet-3DoF