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

Content Provider	Springer Nature Link
Author	Sadeghzadeh, Mehdi Calvert, David Abdullah, Hussein A.
Copyright Year	2014
Abstract	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.
Starting Page	83
Ending Page	104
Page Count	22
File Format	PDF
ISSN	09210296
Journal	Journal of Intelligent & Robotic Systems
Volume Number	78
Issue Number	1
e-ISSN	15730409
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-11-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Visual ervoing Explanation-based neural networks Fuzzy neural networks Q-learning Control, Robotics, Mechatronics Electrical Engineering Artificial Intelligence (incl. Robotics) Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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