Untying cable by combining 3D deep neural network with deep reinforcement learning. (4th March 2023)

• Record Type: Journal Article
• Authors: Fan, Zheming; Shao, Wanpeng; Hayashi, Toyohiro; Ohashi, Takeshi
• Is Part Of: Advanced robotics. Volume 37:Number 5(2023)
• Page Start: 380
• Page End: 394
• Publication Date: 2023-03-04
• Subjects: Cable -- untying operation -- robotic arm -- deep learning reinforcement learning; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2022.2126729
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : In this study, we propose a framework to solve the cable traction problem where many cables are needed, such as in factories, schools, etc. Our framework is divided into three stages: First, we use SegNet to recognize the cable in the image, build a 3D voxel model by 3D-VAE-GAN, and transform the voxel model to a 3D particle model in Unity3D by the Position-Based Dynamics method. Second, features learned by a 3D deep neural network (PointNet++) from the 3D model are fed into a deep reinforcement learning (RL) network (DQN) to get a series of actions to untie the 3D cable model in Unity3D. Finally, we input the action sequence to the robot arm to untie the cable in the real world. Experimental results show that compared with traditional methods, our method has successfully applied artificial intelligence algorithms to help the computer learn how to untie knotted cables by itself in the virtual world. The untying operations learned in the virtual world can be also used to untie the cable in the real world. GRAPHICAL ABSTRACT: UF0001