Feed-forward positioning of musculoskeletal-like robotic systems with muscular viscosity: Determination of an adequate internal force

Abstract

This paper proposes a new feed-forward positioning method for a musculoskeletal-like robotic system considering a muscle-like nonlinear viscosity, and a new determination method of the internal force using the reinforcement learning scheme. In our previous works, a feed-forward positioning method for the musculoskeletal-like robotic systems has been proposed. In the method, the position regulation of the system can be accomplished by inputting a desired internal force balancing at a desired position. It has been quite effective for the muscle-like driven mechanism because no sensor is necessary to regulate the position. However, this method often induces an overshoot phenomenon when performing a set-point control. In addition, there is another intrinsic problem that musculoskeletal-like redundant-driven mechanisms own the ill-posed problems that the internal force is unable to determine uniquely. In this paper, for the farmer problem, a muscle-like nonlinear viscosity is newly added to the controller to reduce such an overshoot phenomenon and then to expand the stable region of the manipulator. For the latter problem, a determination method of the internal force using a reinforcement learning scheme is newly proposed. In what follows, firstly a new feed-forward controller which considers the muscle-like viscosity is introduced, and shows its effectiveness through numerical simulations. Next, the determination method of the internal force using a reinforcement learning scheme is proposed and its effectiveness is also shown through numerical simulations.