Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/110001
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Title: A vision-language-guided and deep reinforcement learning-enabled approach for unstructured human-robot collaborative manufacturing task fulfilment
Authors: Zheng, P 
Li, C 
Fan, J 
Wang, L
Issue Date: 2024
Source: CIRP annals : manufactering technology, 2024, v. 73, no. 1, p. 341-344
Abstract: Human-Robot Collaboration (HRC) has emerged as a pivot in contemporary human-centric smart manufacturing scenarios. However, the fulfilment of HRC tasks in unstructured scenes brings many challenges to be overcome. In this work, mixed reality head-mounted display is modelled as an effective data collection, communication, and state representation interface/tool for HRC task settings. By integrating vision-language cues with large language model, a vision-language-guided HRC task planning approach is firstly proposed. Then, a deep reinforcement learning-enabled mobile manipulator motion control policy is generated to fulfil HRC task primitives. Its feasibility is demonstrated in several HRC unstructured manufacturing tasks with comparative results.
Keywords: Human-guided robot learning
Human-robot collaboration
Manufacturing system
Publisher: Elsevier BV
Journal: CIRP annals : manufactering technology 
ISSN: 0007-8506
EISSN: 1726-0604
DOI: 10.1016/j.cirp.2024.04.003
Rights: © 2024 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
The following publication Zheng, P., Li, C., Fan, J., & Wang, L. (2024). A vision-language-guided and deep reinforcement learning-enabled approach for unstructured human-robot collaborative manufacturing task fulfilment. CIRP Annals, 73(1), 341-344 is available at https://doi.org/10.1016/j.cirp.2024.04.003.
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