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A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations
In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we esta...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583967/ https://www.ncbi.nlm.nih.gov/pubmed/32992888 http://dx.doi.org/10.3390/s20195505 |
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author | Ding, Guanwen Liu, Yubin Zang, Xizhe Zhang, Xuehe Liu, Gangfeng Zhao, Jie |
author_facet | Ding, Guanwen Liu, Yubin Zang, Xizhe Zhang, Xuehe Liu, Gangfeng Zhao, Jie |
author_sort | Ding, Guanwen |
collection | PubMed |
description | In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy. |
format | Online Article Text |
id | pubmed-7583967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75839672020-10-29 A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations Ding, Guanwen Liu, Yubin Zang, Xizhe Zhang, Xuehe Liu, Gangfeng Zhao, Jie Sensors (Basel) Article In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy. MDPI 2020-09-25 /pmc/articles/PMC7583967/ /pubmed/32992888 http://dx.doi.org/10.3390/s20195505 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ding, Guanwen Liu, Yubin Zang, Xizhe Zhang, Xuehe Liu, Gangfeng Zhao, Jie A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations |
title | A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations |
title_full | A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations |
title_fullStr | A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations |
title_full_unstemmed | A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations |
title_short | A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations |
title_sort | task-learning strategy for robotic assembly tasks from human demonstrations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583967/ https://www.ncbi.nlm.nih.gov/pubmed/32992888 http://dx.doi.org/10.3390/s20195505 |
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