A Simple Learning Algorithm for Contact-Rich Robotic Grasping

This paper presents the preliminary results of the work on a control algorithm for a two-finger gripper equipped with an electronic skin (e-skin). The e-skin measures the magnitude and location of the pressure applied to it. Contact localization allowed the development of a reliable control algorithm for robotic grasping. The main contribution of this work is the learning algorithm that adjusts the pose of the gripper during the pre-grasp approach step based on contact information. The algorithm was tested on different objects and showed comparable grasping reliability to the vision-based approach. The developed tactile sensor-rich gripper with a dedicated control algorithm may find applications in various fields, from industrial robotics to advanced interactive robots.