CoAS-Net: Context-Aware Suction Network with a Large-Scale Domain Randomized Synthetic Dataset

• Published in: IEEE robotics and automation letters Vol. 9; no. 1; pp. 1 - 8
• Main Authors: Son, Yeong Gwang, Bui, Tat Hieu, Hong, Juyong, Kim, Yong Hyeon, Moon, Seung Jae, Kim, Chun Soo, Rhee, Issac, Kang, Hansol, Choi, Hyouk Ryeol
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Collision avoidance; Computer Vision for Automation; Context; Data Sets for Robotic Vision; Datasets; Deep Learning in Grasping and Manipulation; Grasping; Grasping (robotics); Object oriented modeling; Outliers (statistics); Picking; Pipelines; Robotics; Robots; Seals; Springs; Suction; Suction Grasping; Synthetic data

Robotic grasping is one of the essential skills in robotics. From industrial to housework, robots are required to handle objects, enabling them to interact with their surroundings. Among the various tasks in robotic grasping, bin-picking is considered one of the most challenging because of the clutt...