Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

Robotic grasping for cluttered tasks and heterogeneous targets is not satisfied by the deep learning that has been developed in the last decade. The main problem lies in intelligence, which is stagnant, even though it has a high accuracy rate in usual environment; however, the cluttered grasping env...

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Autores principales:	Muslikhin, Jenq-Ruey Horng, Szu-Yueh Yang, Ming-Shyan Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Action learning deep learning eye-in-hand manipulator k-nearest neighbor robotic manipulator robotic grasping Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/749f7dd7ae2046d8a728f4e0c6b8a779
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id	oai:doaj.org-article:749f7dd7ae2046d8a728f4e0c6b8a779
record_format	dspace
spelling	oai:doaj.org-article:749f7dd7ae2046d8a728f4e0c6b8a7792021-12-02T00:00:39ZSelf-Correction for Eye-In-Hand Robotic Grasping Using Action Learning2169-353610.1109/ACCESS.2021.3129474https://doaj.org/article/749f7dd7ae2046d8a728f4e0c6b8a7792021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9622215/https://doaj.org/toc/2169-3536Robotic grasping for cluttered tasks and heterogeneous targets is not satisfied by the deep learning that has been developed in the last decade. The main problem lies in intelligence, which is stagnant, even though it has a high accuracy rate in usual environment; however, the cluttered grasping environment is very irregular. In this paper, an action learning for robotic grasping using eye-in-hand coordination is developed to grasp the cluttered and wide range of various objects using 6 degree-of-freedom (DOF) robotic manipulator equipped with a three-finger gripper. To involve action learning in this system, k-Nearest Neighbor (kNN), Disparity Map (DM), and You Only Look Once (YOLO) were needed. After successfully formulating the learning cycle, an instrument assesses the robot’s environment and performance with qualitative weightings. Some experiments of measuring the depth of the target, localization of target variations, target detection, and the gripping process itself were conducted. The entire process is spread out in plan, act, observe, and reflect for each action learning cycle. If the first cycle does not suffice the results according to the minimum pass standard, the cycle will renew until the robot succeeds in picking and placing. Furthermore, this study demonstrated that the action learning-based object manipulation system with stereo-like vision and eye-in-hand calibration can improve intelligence over previous errors with acceptable errors. Thus, action learning might be applicable to other object manipulation systems without having to define the environment first. MuslikhinJenq-Ruey HorngSzu-Yueh YangMing-Shyan WangIEEEarticleAction learningdeep learningeye-in-hand manipulatork-nearest neighborrobotic manipulatorrobotic graspingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156422-156436 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Action learning deep learning eye-in-hand manipulator k-nearest neighbor robotic manipulator robotic grasping Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Action learning deep learning eye-in-hand manipulator k-nearest neighbor robotic manipulator robotic grasping Electrical engineering. Electronics. Nuclear engineering TK1-9971 Muslikhin Jenq-Ruey Horng Szu-Yueh Yang Ming-Shyan Wang Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning
description	Robotic grasping for cluttered tasks and heterogeneous targets is not satisfied by the deep learning that has been developed in the last decade. The main problem lies in intelligence, which is stagnant, even though it has a high accuracy rate in usual environment; however, the cluttered grasping environment is very irregular. In this paper, an action learning for robotic grasping using eye-in-hand coordination is developed to grasp the cluttered and wide range of various objects using 6 degree-of-freedom (DOF) robotic manipulator equipped with a three-finger gripper. To involve action learning in this system, k-Nearest Neighbor (kNN), Disparity Map (DM), and You Only Look Once (YOLO) were needed. After successfully formulating the learning cycle, an instrument assesses the robot’s environment and performance with qualitative weightings. Some experiments of measuring the depth of the target, localization of target variations, target detection, and the gripping process itself were conducted. The entire process is spread out in plan, act, observe, and reflect for each action learning cycle. If the first cycle does not suffice the results according to the minimum pass standard, the cycle will renew until the robot succeeds in picking and placing. Furthermore, this study demonstrated that the action learning-based object manipulation system with stereo-like vision and eye-in-hand calibration can improve intelligence over previous errors with acceptable errors. Thus, action learning might be applicable to other object manipulation systems without having to define the environment first.
format	article
author	Muslikhin Jenq-Ruey Horng Szu-Yueh Yang Ming-Shyan Wang
author_facet	Muslikhin Jenq-Ruey Horng Szu-Yueh Yang Ming-Shyan Wang
author_sort	Muslikhin
title	Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning
title_short	Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning
title_full	Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning
title_fullStr	Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning
title_full_unstemmed	Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning
title_sort	self-correction for eye-in-hand robotic grasping using action learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/749f7dd7ae2046d8a728f4e0c6b8a779
work_keys_str_mv	AT muslikhin selfcorrectionforeyeinhandroboticgraspingusingactionlearning AT jenqrueyhorng selfcorrectionforeyeinhandroboticgraspingusingactionlearning AT szuyuehyang selfcorrectionforeyeinhandroboticgraspingusingactionlearning AT mingshyanwang selfcorrectionforeyeinhandroboticgraspingusingactionlearning
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Self-Correction for Eye-In-Hand Robotic Grasping Using Action Learning

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