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Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model
Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to form...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185262/ https://www.ncbi.nlm.nih.gov/pubmed/35684900 http://dx.doi.org/10.3390/s22114279 |
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author | Zhang, Zhujun Peng, Gaoliang Wang, Weitian Chen, Yi Jia, Yunyi Liu, Shaohui |
author_facet | Zhang, Zhujun Peng, Gaoliang Wang, Weitian Chen, Yi Jia, Yunyi Liu, Shaohui |
author_sort | Zhang, Zhujun |
collection | PubMed |
description | Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to formulate the robot to surmise what kind of assistance a human coworker needs and naturally take the right action at the right time. This paper proposes a prediction-based human-robot collaboration model for assembly scenarios. An embedded learning from demonstration technique enables the robot to understand various task descriptions and customized working preferences. A state-enhanced convolutional long short-term memory (ConvLSTM)-based framework is formulated for extracting the high-level spatiotemporal features from the shared workspace and predicting the future actions to facilitate the fluent task transition. This model allows the robot to adapt itself to predicted human actions and enables proactive assistance during collaboration. We applied our model to the seats assembly experiment for a scale model vehicle and it can obtain a human worker’s intentions, predict a coworker’s future actions, and provide assembly parts correspondingly. It has been verified that the proposed framework yields higher smoothness and shorter idle times, and meets more working styles, compared to the state-of-the-art methods without prediction awareness. |
format | Online Article Text |
id | pubmed-9185262 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91852622022-06-11 Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model Zhang, Zhujun Peng, Gaoliang Wang, Weitian Chen, Yi Jia, Yunyi Liu, Shaohui Sensors (Basel) Article Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to formulate the robot to surmise what kind of assistance a human coworker needs and naturally take the right action at the right time. This paper proposes a prediction-based human-robot collaboration model for assembly scenarios. An embedded learning from demonstration technique enables the robot to understand various task descriptions and customized working preferences. A state-enhanced convolutional long short-term memory (ConvLSTM)-based framework is formulated for extracting the high-level spatiotemporal features from the shared workspace and predicting the future actions to facilitate the fluent task transition. This model allows the robot to adapt itself to predicted human actions and enables proactive assistance during collaboration. We applied our model to the seats assembly experiment for a scale model vehicle and it can obtain a human worker’s intentions, predict a coworker’s future actions, and provide assembly parts correspondingly. It has been verified that the proposed framework yields higher smoothness and shorter idle times, and meets more working styles, compared to the state-of-the-art methods without prediction awareness. MDPI 2022-06-03 /pmc/articles/PMC9185262/ /pubmed/35684900 http://dx.doi.org/10.3390/s22114279 Text en © 2022 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 Zhang, Zhujun Peng, Gaoliang Wang, Weitian Chen, Yi Jia, Yunyi Liu, Shaohui Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model |
title | Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model |
title_full | Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model |
title_fullStr | Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model |
title_full_unstemmed | Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model |
title_short | Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model |
title_sort | prediction-based human-robot collaboration in assembly tasks using a learning from demonstration model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185262/ https://www.ncbi.nlm.nih.gov/pubmed/35684900 http://dx.doi.org/10.3390/s22114279 |
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