Pose-Based Tactile Servoing: Controlled Soft Touch Using Deep Learning

摘要

This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features, such as edges and surfaces. PBTS control is implemented with a soft, curved optical tactile sensor [the Bristol Robotics Laboratory (BRL) TacTip] using a convolutional neural network trained to be insensitive to shear. As a consequence, robust and accurate controlled motion over various complex 3D objects is attained.