An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing
Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along...
Ausführliche Beschreibung
Autor*in: |
Zaatari, Shirine El [verfasserIn] |
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Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 33(2021), 5 vom: 06. Feb., Seite 1503-1519 |
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Übergeordnetes Werk: |
volume:33 ; year:2021 ; number:5 ; day:06 ; month:02 ; pages:1503-1519 |
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DOI / URN: |
10.1007/s10845-021-01743-w |
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Katalog-ID: |
OLC2078554146 |
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10.1007/s10845-021-01743-w doi (DE-627)OLC2078554146 (DE-He213)s10845-021-01743-w-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zaatari, Shirine El verfasserin aut An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications. Learning from demonstration Reinforcement learning Collaborative robots Wang, Yuqi aut Hu, Yudie aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2021), 5 vom: 06. Feb., Seite 1503-1519 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2021 number:5 day:06 month:02 pages:1503-1519 https://doi.org/10.1007/s10845-021-01743-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2021 5 06 02 1503-1519 |
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10.1007/s10845-021-01743-w doi (DE-627)OLC2078554146 (DE-He213)s10845-021-01743-w-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zaatari, Shirine El verfasserin aut An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications. Learning from demonstration Reinforcement learning Collaborative robots Wang, Yuqi aut Hu, Yudie aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2021), 5 vom: 06. Feb., Seite 1503-1519 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2021 number:5 day:06 month:02 pages:1503-1519 https://doi.org/10.1007/s10845-021-01743-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2021 5 06 02 1503-1519 |
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10.1007/s10845-021-01743-w doi (DE-627)OLC2078554146 (DE-He213)s10845-021-01743-w-p DE-627 ger DE-627 rakwb eng 620 004 VZ Zaatari, Shirine El verfasserin aut An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications. Learning from demonstration Reinforcement learning Collaborative robots Wang, Yuqi aut Hu, Yudie aut Li, Weidong (orcid)0000-0001-5559-7834 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 33(2021), 5 vom: 06. Feb., Seite 1503-1519 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:33 year:2021 number:5 day:06 month:02 pages:1503-1519 https://doi.org/10.1007/s10845-021-01743-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 33 2021 5 06 02 1503-1519 |
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An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing |
abstract |
Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
abstractGer |
Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
abstract_unstemmed |
Abstract Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
collection_details |
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container_issue |
5 |
title_short |
An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing |
url |
https://doi.org/10.1007/s10845-021-01743-w |
remote_bool |
false |
author2 |
Wang, Yuqi Hu, Yudie Li, Weidong |
author2Str |
Wang, Yuqi Hu, Yudie Li, Weidong |
ppnlink |
130892815 |
mediatype_str_mv |
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isOA_txt |
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hochschulschrift_bool |
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doi_str |
10.1007/s10845-021-01743-w |
up_date |
2024-07-03T21:00:46.660Z |
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1803593131398004736 |
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7.400731 |