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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Science Extension (IASE)
2019
|
| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848787154334908416 |
|---|---|
| author | AlDahoul, Nouar Htike@Muhammad Yusof, Zaw Zaw |
| author_facet | AlDahoul, Nouar Htike@Muhammad Yusof, Zaw Zaw |
| author_sort | AlDahoul, Nouar |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:20:25Z |
| format | Article |
| id | iium-69667 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:20:25Z |
| publishDate | 2019 |
| publisher | Institute of Advanced Science Extension (IASE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 |
| spellingShingle | Q350 Information theory AlDahoul, Nouar Htike@Muhammad Yusof, Zaw Zaw Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting |
| title | 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_short | Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting |
| title_sort | utilizing hierarchical extreme learning machine based reinforcement learning for object sorting |
| topic | Q350 Information theory |
| 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 |