A modular reinforcement-based neural controller for a three-link manipulator

Abstract

This paper presents a modular neural controller that learns goal-oriented obstacle-avoiding motion strategies for a sensor-based three-link planar robot arm. It acquires these strategies through reinforcement learning from local sensory data. The controller has two reinforcement-based modules: a module for negotiating obstacles and a module for moving to the goal. Both modules generate actions that are interpreted with regard to a goal vector in the robot joint space. A differential inverse kinematics (DIV) module is used to obtain such a goal vector. The DIV module is based on the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics in polar coordinates. The controller achieves a satisfactory performance quite rapidly and shows good generalization capabilities in the face of new environments.