Learning-based Six-axis Force/Torque Estimation Using GelStereo Fingertip Visuotactile Sensing
Chaofan Zhang, Shaowei Cui, Yinghao Cai, Jingyi Hu, Rui Wang, Shuo Wang
Abstract
Visuotactile sensors have recently attracted much attention in robot communities due to the benefit of high spatial resolution sensing. However, force/torque estimation by visuotactile sensors remains a challenging problem. In this paper, we propose a learning-based six-axis force/torque estimation network using GelStereo visuotactile sensor, which can provide two-dimensional (2D) and three-dimensional (3D) displacements of markers embedded in the sensor surface. The convolutional neural networks are employed to extract multi-modal tactile deformation features; and a novel contact positional encoding method is proposed to eliminate the influence of translation invariance in convolutional operators. The well-trained model achieves the best RMSE of 0.290 N in force and 0.0084 Nm in torque. Furthermore, the proposed force/torque estimation network is integrated with a force-feedback policy for adaptive grasping tasks. The experimental results demonstrate the effectiveness of the proposed method and its potential application in robotic grasping and manipulation tasks.