Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/110001
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | - |
dc.creator | Zheng, P | - |
dc.creator | Li, C | - |
dc.creator | Fan, J | - |
dc.creator | Wang, L | - |
dc.date.accessioned | 2024-11-20T07:30:48Z | - |
dc.date.available | 2024-11-20T07:30:48Z | - |
dc.identifier.issn | 0007-8506 | - |
dc.identifier.uri | http://hdl.handle.net/10397/110001 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.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/) | en_US |
dc.rights | 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. | en_US |
dc.subject | Human-guided robot learning | en_US |
dc.subject | Human-robot collaboration | en_US |
dc.subject | Manufacturing system | en_US |
dc.title | A vision-language-guided and deep reinforcement learning-enabled approach for unstructured human-robot collaborative manufacturing task fulfilment | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 341 | - |
dc.identifier.epage | 344 | - |
dc.identifier.volume | 73 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1016/j.cirp.2024.04.003 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | CIRP annals : manufactering technology, 2024, v. 73, no. 1, p. 341-344 | - |
dcterms.isPartOf | CIRP annals : manufactering technology | - |
dcterms.issued | 2024 | - |
dc.identifier.scopus | 2-s2.0-85190754943 | - |
dc.identifier.eissn | 1726-0604 | - |
dc.description.validate | 202411 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S0007850624000180-main.pdf | 1.29 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.