Towards learning by demonstration for industrial assembly tasks

Moreno, VH; Carmichael, MG; Deuse, J

Towards learning by demonstration for industrial assembly tasks

Moreno, VH Carmichael, MG Deuse, J

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Publication Type:: Chapter
Citation:: Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, 2023, pp. 229-239
Issue Date:: 2023-07-10

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Field	Value	Language
dc.contributor.author	Moreno, VH
dc.contributor.author	Carmichael, MG
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.date.accessioned	2024-06-24T01:51:48Z
dc.date.available	2024-06-24T01:51:48Z
dc.date.issued	2023-07-10
dc.identifier.citation	Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, 2023, pp. 229-239
dc.identifier.uri	http://hdl.handle.net/10453/179630
dc.description.abstract	In recent times, learning by demonstration has seen tremendous progress in robotic assembly operations. One of themost prominent trajectory-level taskmodels applied isDynamic Movement Primitives (DMP). However, it lacks the ability to tackle complex operations as often encountered in industrial assembly. Augmenting low-level models with a highlevel framework in which different movement segments are deliberately parameterised is considered promising for such scenarios. This paper investigates the combination of trajectory-level DMPs with Methods-Time Measurement (MTM). We demonstrate how theMTM-1 system is utilised to establish distinguished DMP models for five of its basic elements, paving the way to benefitting from the sophisticated MTM system. The evaluation of the framework is conducted on a generic pick and place operation. Compared to a one-model-fits-all DMP approach for the whole task, the proposed method shows the advantage of appropriate temporal scaling, accuracy levelling and force consideration at adequate times.
dc.language	en
dc.relation.ispartof	Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022
dc.relation.isbasedon	10.1007/9783031100710_19
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Towards learning by demonstration for industrial assembly tasks
dc.type	Chapter
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - RI - Robotics Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAM - Centre for Advanced Manufacturing
utslib.copyright.status	in_progress	*
dc.date.updated	2024-06-24T01:51:47Z
pubs.publication-status	Published

Abstract:

In recent times, learning by demonstration has seen tremendous progress in robotic assembly operations. One of themost prominent trajectory-level taskmodels applied isDynamic Movement Primitives (DMP). However, it lacks the ability to tackle complex operations as often encountered in industrial assembly. Augmenting low-level models with a highlevel framework in which different movement segments are deliberately parameterised is considered promising for such scenarios. This paper investigates the combination of trajectory-level DMPs with Methods-Time Measurement (MTM). We demonstrate how theMTM-1 system is utilised to establish distinguished DMP models for five of its basic elements, paving the way to benefitting from the sophisticated MTM system. The evaluation of the framework is conducted on a generic pick and place operation. Compared to a one-model-fits-all DMP approach for the whole task, the proposed method shows the advantage of appropriate temporal scaling, accuracy levelling and force consideration at adequate times.

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http://hdl.handle.net/10453/179630