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A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation
Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708703/ https://www.ncbi.nlm.nih.gov/pubmed/34960435 http://dx.doi.org/10.3390/s21248341 |
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author | Huang, Zebin Wang, Ziwei Bai, Weibang Huang, Yanpei Sun, Lichao Xiao, Bo Yeatman, Eric M. |
author_facet | Huang, Zebin Wang, Ziwei Bai, Weibang Huang, Yanpei Sun, Lichao Xiao, Bo Yeatman, Eric M. |
author_sort | Huang, Zebin |
collection | PubMed |
description | Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation. |
format | Online Article Text |
id | pubmed-8708703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87087032021-12-25 A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation Huang, Zebin Wang, Ziwei Bai, Weibang Huang, Yanpei Sun, Lichao Xiao, Bo Yeatman, Eric M. Sensors (Basel) Article Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation. MDPI 2021-12-14 /pmc/articles/PMC8708703/ /pubmed/34960435 http://dx.doi.org/10.3390/s21248341 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Zebin Wang, Ziwei Bai, Weibang Huang, Yanpei Sun, Lichao Xiao, Bo Yeatman, Eric M. A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation |
title | A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation |
title_full | A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation |
title_fullStr | A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation |
title_full_unstemmed | A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation |
title_short | A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation |
title_sort | novel training and collaboration integrated framework for human–agent teleoperation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708703/ https://www.ncbi.nlm.nih.gov/pubmed/34960435 http://dx.doi.org/10.3390/s21248341 |
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