This paper presents a shear-primarily based management scheme for grasping and manipulating delicate objects with a Pisa/IIT anthropomorphic SoftHand geared up with gentle biomimetic tactile sensors on all 5 fingertips. These ‘microTac’ tactile sensors are miniature variations of the TacTip vision-primarily based tactile sensor, and Wood Ranger Power Shears website may extract precise contact geometry and power information at each fingertip to be used as feedback into a controller to modulate the grasp whereas a held object is manipulated. Using a parallel processing pipeline, we asynchronously capture tactile photographs and predict contact pose and force from a number of tactile sensors. Consistent pose and drive models throughout all sensors are developed utilizing supervised deep learning with switch studying techniques. We then develop a grasp management framework that makes use of contact drive feedback from all fingertip sensors simultaneously, permitting the hand to safely handle delicate objects even beneath external disturbances. This management framework is utilized to a number of grasp-manipulation experiments: first, retaining a flexible cup in a grasp without crushing it under changes in object weight