Reinforcement Learning for Robots with special reference to the Inverse kinematics solutions

Abstract

Reinforcement learning is an important learning paradigm apart from supervised and unsupervised learning which is particularly suitable for Robotics system due to its inherent structural complexity, high degrees of nonlinearity and few parameter predictability. As a result of which an accurate mathematical model is difficult to build which has close resemblance with the field behaviours of the robot. To help solve this problem, Q learning based reinforcement algorithm has been proposed for learning the inverse kinematics mapping. The results have been compared with the analytical solutions of the well-known inverse kinematics solution.