Grasp pose detection for deformable daily items by pix2stiffness estimation. (18th June 2022) - Makihara, Koshi; Domae, Yukiyasu; Ramirez-Alpizar, Ixchel G.; Ueshiba, Toshio; Harada, Kensuke
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