Data-efficient multimodal human action recognition for proactive human–robot collaborative assembly: A cross-domain few-shot learning approach-Reference-Cited by-同舟云学术

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Data-efficient multimodal human action recognition for proactive human–robot collaborative assembly: A cross-domain few-shot learning approach

Published:2024-10 Issue: Volume:89 Page:102785
ISSN:0736-5845
Container-title:Robotics and Computer-Integrated Manufacturing
language:en
Short-container-title:Robotics and Computer-Integrated Manufacturing

Author:

Wang Tianyu,Liu Zhihao,Wang Lihui,Li Mian,Wang Xi Vincent^ORCID

Publisher

Elsevier BV

Reference54 articles.

1. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives;Zheng;Front. Mech. Eng.,2018

2. Challenges and opportunities in human robot collaboration context of Industry 4.0 - a state of the art review;Inkulu;Ind. Robot: Int. J. Robot. Res. Appl.,2022

3. Symbiotic human-robot collaborative assembly;Wang;CIRP Ann,2019

4. Industry 5.0: Prospect and retrospect;Leng;J. Manuf. Syst.,2022

5. Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives;Li;Robot. Comput.-Integr. Manuf.,2023

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