Attribute-Based Robotic Grasping With Data-Efficient Adaptation

• Published in: IEEE transactions on robotics Vol. 40; pp. 1566 - 1579
• Main Authors: Yang, Yang, Yu, Houjian, Lou, Xibai, Liu, Yuanhao, Choi, Changhyun
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Affordances; Clutter; Coders; Data augmentation; Data models; Deep learning; Deep learning in grasping and manipulation; Grasping; Grasping (robotics); perception for grasping and manipulation; Robotics; Robots; Visualization

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target object in clutter remains challenging. This article attempts to address the challenge by leveraging object attributes t...