MODEL-FREE ONLINE REINFORCEMENT LEARNING OF A ROBOTIC MANIPULATOR

For robotic systems that do not have joint torques as direct inputs, the computed torque approach, often based on a general dynamic model that is linear with respect to joint velocities and accelerations, is very difficult to implement. We have applied a general model-free online reinforcement learning control methodology for discrete nonaffine nonlinear multiple-input-multiple-output systems to a second-order robotic system. The controller produced effective trajectory tracking in simulations of a two-degree-of-freedom robotic arm, and in actual experiments, with a three degree-of-freedom robotic arm.