Grasp State Assessment of Deformable Objects Using Visual-Tactile Fusion Perception
Shaowei Cui,Rui Wang,Junhang Wei,Fanrong Li,Shuo Wang,Shaowei Cui,Rui Wang,Junhang Wei,Fanrong Li,Shuo Wang
Humans can quickly determine the force required to grasp a deformable object to prevent its sliding or excessive deformation through vision and touch, which is still a challenging task for robots. To address this issue, we propose a novel 3D convolution-based visual-tactile fusion deep neural network (C3D-VTFN) to evaluate the grasp state of various deformable objects in this paper. Specifically, ...
