Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting

Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting

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 vegeta...

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Main Authors: AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw
Format: Article
Language:English
English
Published: Institute of Advanced Science Extension (IASE) 2019
Subjects:
Q350 Information theory
Online Access:http://irep.iium.edu.my/69667/
http://irep.iium.edu.my/69667/
http://irep.iium.edu.my/69667/
http://irep.iium.edu.my/69667/1/69667_Utilizing%20hierarchical%20extreme%20learning%20machine.pdf
http://irep.iium.edu.my/69667/2/69667_Utilizing%20hierarchical%20extreme%20learning%20machine_WOS.pdf
id iium-69667
recordtype eprints
spelling iium-696672019-03-08T02:41:30Z http://irep.iium.edu.my/69667/ Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting AlDahoul, Nouar Htike@Muhammad Yusof, Zaw Zaw Q350 Information theory 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. Institute of Advanced Science Extension (IASE) 2019-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/69667/1/69667_Utilizing%20hierarchical%20extreme%20learning%20machine.pdf application/pdf en http://irep.iium.edu.my/69667/2/69667_Utilizing%20hierarchical%20extreme%20learning%20machine_WOS.pdf 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 http://science-gate.com/IJAAS/Articles/2019/2019-6-1/1021833ijaas201901015.pdf 10.21833/ijaas.2019.01.015
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic Q350 Information theory
spellingShingle Q350 Information theory
AlDahoul, Nouar
Htike@Muhammad Yusof, Zaw Zaw
Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
description 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.
format Article
author AlDahoul, Nouar
Htike@Muhammad Yusof, Zaw Zaw
author_facet AlDahoul, Nouar
Htike@Muhammad Yusof, Zaw Zaw
author_sort AlDahoul, Nouar
title Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
title_short Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
title_full Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
title_fullStr Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
title_full_unstemmed Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
title_sort utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
publisher Institute of Advanced Science Extension (IASE)
publishDate 2019
url http://irep.iium.edu.my/69667/
http://irep.iium.edu.my/69667/
http://irep.iium.edu.my/69667/
http://irep.iium.edu.my/69667/1/69667_Utilizing%20hierarchical%20extreme%20learning%20machine.pdf
http://irep.iium.edu.my/69667/2/69667_Utilizing%20hierarchical%20extreme%20learning%20machine_WOS.pdf
first_indexed 2023-09-18T21:38:53Z
last_indexed 2023-09-18T21:38:53Z
_version_ 1777413006026604544

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