Operational learning with sensory feedback for controlling a robotic thumb using the posterior auricular muscle

Abstract

Extra robotic limbs in a robotic system that are designed to augment and expand human abilities have received attention in the field of wearable robotics. We aim to develop a robotic system that is controlled by body parts that are not used on a daily basis in order to augment and expand human abilities. This paper presents operational learning experiments for manipulating a robotic thumb using the posterior auricular muscle, a body part that is not used in everyday life. In these experiments, reaching motions were executed using sensory feedback in the robotic thumb through a device that continuously displays its position. The experimental results indicate the proposed operational learning experiments improve the ability to contract the posterior auricular muscles. In addition, the results indicate the operability of a robotic thumb could be improved by acquiring internal models through repetitive operational learning.