Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration using Hierarchical BiLSTM Classifier

Chen, CS; Chen, SK; Lin, CT; Lai, CC

Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration using Hierarchical BiLSTM Classifier

Chen, CS Chen, SK Lin, CT Lai, CC

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2020, 6, (2), pp. 502-509
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Chen, CS
dc.contributor.author	Chen, SK
dc.contributor.author	Lin, CT
dc.contributor.author	Lai, CC
dc.date.accessioned	2021-01-29T02:36:21Z
dc.date.available	2021-01-29T02:36:21Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2020, 6, (2), pp. 502-509
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/145648
dc.description.abstract	IEEE Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recently LfD research, machine vision is usually used to capture the human-robot interaction. However, it's not reliable during the machining process. In this paper, a novel intuitive high-level kinesthetic teaching technology is proposed by reconstructing the recorded motion information during a human-guided robotic arm. A hierarchical BiLSTM-based machine learning algorithm is proposed in this paper to recognize and segment motion primitives according to the therblig definition. The hybrid sensing interface is used to record and extract the motion features, the velocity profile, force/torque, and gripper information. The motion features, output data via the hybrid sensing interface, are finally used to classify into the target motion primitive by the proposed classifier. The experimental results and comparisons with the state-of-the-art algorithm show that the proposed method can correctly and efficiently synthesize the recorded motion features into a motion primitive sequence. Finally, the recognition results of real-world tasks show that the proposed algorithm can be used to reconstruct the human-guided task and further used to robot command for the KUKA robot. The experimental results of the reconstructed trajectory show that a real-world task can represent and maintain the accuracy in 2.37 mm using the proposed algorithm.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2020.3047772
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0913 Mechanical Engineering
dc.title	Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration using Hierarchical BiLSTM Classifier
dc.type	Journal Article
utslib.citation.volume	6
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-29T02:36:16Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	2

Abstract:

IEEE Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recently LfD research, machine vision is usually used to capture the human-robot interaction. However, it's not reliable during the machining process. In this paper, a novel intuitive high-level kinesthetic teaching technology is proposed by reconstructing the recorded motion information during a human-guided robotic arm. A hierarchical BiLSTM-based machine learning algorithm is proposed in this paper to recognize and segment motion primitives according to the therblig definition. The hybrid sensing interface is used to record and extract the motion features, the velocity profile, force/torque, and gripper information. The motion features, output data via the hybrid sensing interface, are finally used to classify into the target motion primitive by the proposed classifier. The experimental results and comparisons with the state-of-the-art algorithm show that the proposed method can correctly and efficiently synthesize the recorded motion features into a motion primitive sequence. Finally, the recognition results of real-world tasks show that the proposed algorithm can be used to reconstruct the human-guided task and further used to robot command for the KUKA robot. The experimental results of the reconstructed trajectory show that a real-world task can represent and maintain the accuracy in 2.37 mm using the proposed algorithm.

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http://hdl.handle.net/10453/145648