Self-Learning Visual Servoing of Robot Manipulator Using Explanation-Based Fuzzy Neural Networks and Q-Learning

摘要

A new self-learning visual servoing system for the robot manipulators is proposed. This system includes two main properties: on-line self training and lifelong learning that are implemented by the Q-Learning algorithm and Explanation-based Fuzzy Neural Networks (EBFNN) respectively. We demonstrate that the number of training samples and the training time for a specific robot positioning accuracy can be reduced using explanation-based fuzzy neural networks and the Q-Learning algorithm. The system uses Q-learning to find the optimal policy in conjunction with the reinforcement learning. This policy is used by a robot to reach an object that has been randomly placed in a static workspace. Background knowledge about the robot and its environment is transferred to the robot agent during the learning process using a set of previously trained neural networks. This system learns the optimal policy in order to select the best action that maximizes the cumulative reward received at each time step. This learning approach does not use either a robot or camera model, or require calibration. Simulation results prove the effectiveness of this methodology to improve the learning process and the performance of the self-learning visual servoing system.