A learning and dynamic pattern generating architecture for skilful robotic baseball batting system

Abstract

A learning and dynamic pattern generating system for acquiring the skills in dynamic manipulation of objects using robotic manipulators is to be established, where the desired space trajectories for the manipulators are not specified explicitly. Robotic batting is taken as a task example. The problem is approached so as to result in an iterative learning of the joint driving torque patterns of the manipulator that are considered as the task skills and learned against several typically given desired ball velocities. A multi-layered artificial neural network is used to learn and generalize the joint driving torque against various desired ball velocities, and an iterative optimal control algorithm is adopted to generate the supervisory joint driving torque signals for the neural network. Computer simulation examples of a three-degree-of-freedom manipulator are outlined, the results are depicted to explain the idea and verify the proposed approach, and the robustness issues are discussed qualitatively.