Visual-Tactile Fusion for Robotic Stable Grasping

Abstract

The stable grasp is the basis of robotic manipulation. It requires balance of the contact forces and the operated object. The status of the grasp determined by vision is direct according to the object ’ s shape or texture, but quite challenging. The tactile sensor can provide the effective way. In this work, we propose the visual-tactile fusion framework for predicting the grasp. Meanwhile, the object intrinsic property is also used. More than 2550 grasping trials using a novel robot hand with multiple tactile sensors are collected. And visual-tactile intrinsic deep neural network (DNN) is evaluated to prove the performance. The experimental results show the superiority of the proposed method.