Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94646
Title: | Transfer learning-enabled action recognition for human-robot collaborative assembly | Authors: | Li, S Fan, J Zheng, P Wang, L |
Issue Date: | 2021 | Source: | Procedia CIRP, 2021, v. 104, p. 1795-1800 | Abstract: | Human-robot collaboration (HRC) is critical to today's tendency towards high-flexible assembly in manufacturing. Human action recognition, as one of the core prerequisites for HRC, enables industrial robots to understand human intentions and to execute planning adaptively. However, existing deep learning-based action recognition methods rely heavily on a huge amount of annotation data, which may not be effective or realistic in practice. Therefore, a transfer learning-enabled action recognition approach is proposed in this research to facilitate robot reactive control in HRC assembly. Meanwhile, a decision-making mechanism for robotic planning is introduced as well. Lastly, the proposed approach is evaluated in an aircraft bracket assembly scenario to reveal its significance. | Keywords: | Action recognition Domain adaptation Human-robot collaboration assembly Transfer learning |
Publisher: | Elsevier | Journal: | Procedia CIRP | ISSN: | 2212-8271 | DOI: | 10.1016/j.procir.2021.11.303 | Description: | 54th CIRP Conference on Manufacturing System, 22nd-24th September 2021, Greece | Rights: | © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) The following publication Li, S., Fan, J., Zheng, P., & Wang, L. (2021). Transfer learning-enabled action recognition for human-robot collaborative assembly. Procedia CIRP, 104, 1795-1800. is available at https://doi.org/10.1016/j.procir.2021.11.303 |
Appears in Collections: | Conference Paper |
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1-s2.0-S2212827121012014-main.pdf | 1.53 MB | Adobe PDF | View/Open |
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