Grasp pose detection for deformable daily items by pix2stiffness estimation. (18th June 2022)

• Record Type: Journal Article
• Authors: Makihara, Koshi; Domae, Yukiyasu; Ramirez-Alpizar, Ixchel G.; Ueshiba, Toshio; Harada, Kensuke
• Is Part Of: Advanced robotics. Volume 36:Number 12(2022)
• Page Start: 600
• Page End: 610
• Publication Date: 2022-06-18
• Subjects: Grasping -- deformable objects -- stiffness estimation -- image translation -- GAN; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2022.2078669
• Languages: English
• ISSNs: 0169-1864

Abstract

ABSTRACT: While most of the existing works on grasp pose detection have assumed a complete 3D object model, this paper proposes a grasp pose detection method for unknown deformable objects, based on visual information. The proposed method is comprised two parts; (1) pix2stiffness estimation, which generates a stiffness map that indicates the object's stiffness for each pixel in an image using generative adversarial networks (GAN), and (2) grasp pose detection, which adapts a stiffness map to maximally reduce the object's deformation and avoid any possible damage. We demonstrate the validity of the proposed method and evaluate the estimation accuracy via simulations, and in a real environment. We also verify that the proposed approach can plan how to grasp an object using a few 3D models of objects. GRAPHICAL ABSTRACT: UF0001