Human-Robot Interaction Control Using Reinforcement Learning

This book gives brief overview of human-robot interaction control schemes, and presents novel model-free and reinforcement learning controllers. It begins with a brief introduction and state of art of human-robot interaction control and reinforcement learning. It then moves on to describe the typical environment model and some of the most famous identification techniques for parameters estimation. Later chapters address the robot-interaction schemes using impedance and admittance controllers, model-free controllers, and input forces/torques of the human operator. The authors also describe using the reinforcement learning approach for the position/force control task in discrete time, to achieve an optimal robot-environment interaction using a position/force control. They also explore how to design robust controllers based on the modified reinforcement learning under the worst-case uncertainty. Closing topics include inverse and velocity kinematics solution, H2 neural control, and future developments in the field.