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

• Published in: IEEE robotics & automation magazine Vol. 28; no. 4; pp. 43 - 55
• Main Authors: Lepora, Nathan F., Lloyd, John
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolutional neural networks; Deep learning; Feature extraction; Optical fiber networks; Robot sensing systems; Robotics; Robust control; Servocontrol; Servomotors; Tactile discrimination; Tactile sensors; Tactile sensors (robotics); Three dimensional motion; Three-dimensional displays; Touch; Visualization
• ISSN: 1070-9932; 1558-223X
• DOI: 10.1109/MRA.2021.3096141

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.