Active Learning-Aided Design of a Flexible Tactile Sensor Array for Recognizing Properties of Deformable Objects

Recognizing the physical properties of deformable objects poses great challenges to the density and sensitivity of tactile sensors. Monolithic active layer inevitably introduces large electrical crosstalk to high-density sensors, and the traditional trial-and-error method is inefficient in exploring the sensor recipes with optimum sensitivity. In this work, we present the design and implementation of a high-density flexible tactile sensor array. The structured conductive polymer on parallel electrodes was designed to reduce the electrical crosstalk. Meanwhile, an active learning approach was utilized to efficiently explore the relationship between sensor sensitivity and recipes, so as to find the optimum sensor sensitivity. For applications of the sensor array, a tendon-driven gripper with flexible joints was built, where the sensor array was attached to the fingertip. Experiments on recognizing the size and stiffness of deformable objects were conducted to validate the effectiveness of the sensor array. The results indicate that the design paradigm is expected to promote the exploration and applications of tactile sensors.