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Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting

AlDahoul, Nouar and Htike@Muhammad Yusof, Zaw Zaw (2019) Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting. International Journal of Advanced and Applied Sciences, 6 (1). pp. 106-113. ISSN 2313-626X E-ISSN 2313-3724

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Abstract

Automatic and intelligent object sorting is an important task that can sort different objects without human intervention, using the robot arm to carry each object from one location to another. These objects vary in colours, shapes, sizes and orientations. Many applications, such as fruit and vegetable grading, flower grading, and biopsy image grading depend on sorting for a structural arrangement. Traditional machine learning methods, with extracting handcrafted features, are used for this task. Sometimes, these features are not discriminative because of the environmental factors, such as light change. In this study, Hierarchical Extreme Learning Machine (HELM) is utilized as an unsupervised feature learning to learn the object observation directly, and HELM was found to be robust against external change. Reinforcement learning (RL) is used to find the optimal sorting policy that maps each object image to the object’s location. The reason for utilizing RL is lack of output labels in this automatic task. The learning is done sequentially in many episodes. At each episode, the accuracy of sorting is increased to reach the maximum level at the end of learning. The experimental results demonstrated that the proposed HELM-RL sorting can provide the same accuracy as the labelled supervised HELM method after many episodes.

Item Type: Article (Journal)
Additional Information: 6919/69667
Uncontrolled Keywords: Object sorting, Reinforcement learning, Hierarchical extreme learning-machine, Deep learning Feature learning
Subjects: Q Science > Q Science (General) > Q300 Cybernetics > Q350 Information theory
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: . . .
Date Deposited: 22 Jan 2019 13:41
Last Modified: 08 Mar 2019 10:41
URI: http://irep.iium.edu.my/id/eprint/69667

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