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A hybrid and scalable brain-inspired robotic platform
Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further prog...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584638/ https://www.ncbi.nlm.nih.gov/pubmed/33097742 http://dx.doi.org/10.1038/s41598-020-73366-9 |
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author | Zou, Zhe Zhao, Rong Wu, Yujie Yang, Zheyu Tian, Lei Wu, Shuang Wang, Guanrui Yu, Yongchao Zhao, Qi Chen, Mingwang Pei, Jing Chen, Feng Zhang, Youhui Song, Sen Zhao, Mingguo Shi, Luping |
author_facet | Zou, Zhe Zhao, Rong Wu, Yujie Yang, Zheyu Tian, Lei Wu, Shuang Wang, Guanrui Yu, Yongchao Zhao, Qi Chen, Mingwang Pei, Jing Chen, Feng Zhang, Youhui Song, Sen Zhao, Mingguo Shi, Luping |
author_sort | Zou, Zhe |
collection | PubMed |
description | Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots’ intelligence. |
format | Online Article Text |
id | pubmed-7584638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75846382020-10-27 A hybrid and scalable brain-inspired robotic platform Zou, Zhe Zhao, Rong Wu, Yujie Yang, Zheyu Tian, Lei Wu, Shuang Wang, Guanrui Yu, Yongchao Zhao, Qi Chen, Mingwang Pei, Jing Chen, Feng Zhang, Youhui Song, Sen Zhao, Mingguo Shi, Luping Sci Rep Article Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots’ intelligence. Nature Publishing Group UK 2020-10-23 /pmc/articles/PMC7584638/ /pubmed/33097742 http://dx.doi.org/10.1038/s41598-020-73366-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zou, Zhe Zhao, Rong Wu, Yujie Yang, Zheyu Tian, Lei Wu, Shuang Wang, Guanrui Yu, Yongchao Zhao, Qi Chen, Mingwang Pei, Jing Chen, Feng Zhang, Youhui Song, Sen Zhao, Mingguo Shi, Luping A hybrid and scalable brain-inspired robotic platform |
title | A hybrid and scalable brain-inspired robotic platform |
title_full | A hybrid and scalable brain-inspired robotic platform |
title_fullStr | A hybrid and scalable brain-inspired robotic platform |
title_full_unstemmed | A hybrid and scalable brain-inspired robotic platform |
title_short | A hybrid and scalable brain-inspired robotic platform |
title_sort | hybrid and scalable brain-inspired robotic platform |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584638/ https://www.ncbi.nlm.nih.gov/pubmed/33097742 http://dx.doi.org/10.1038/s41598-020-73366-9 |
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