中文版 | English
Title

GraspGPT: Leveraging Semantic Knowledge From a Large Language Model for Task-Oriented Grasping

Author
Publication Years
2023-11
DOI
Source Title
ISSN
2377-3774
Volume8Issue:11Pages:7551-7558
Keywords
URL[Source Record]
Indexed By
SUSTech Authorship
First
WOS Accession No
WOS:001089173600011
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10265134
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/575775
Affiliation
1.Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China
2.Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA
First Author AffilicationSouthern University of Science and Technology
First Author's First AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Chao Tang,Dehao Huang,Wenqi Ge,et al. GraspGPT: Leveraging Semantic Knowledge From a Large Language Model for Task-Oriented Grasping[J]. IEEE Robotics and Automation Letters,2023,8(11):7551-7558.
APA
Chao Tang,Dehao Huang,Wenqi Ge,Weiyu Liu,&Hong Zhang.(2023).GraspGPT: Leveraging Semantic Knowledge From a Large Language Model for Task-Oriented Grasping.IEEE Robotics and Automation Letters,8(11),7551-7558.
MLA
Chao Tang,et al."GraspGPT: Leveraging Semantic Knowledge From a Large Language Model for Task-Oriented Grasping".IEEE Robotics and Automation Letters 8.11(2023):7551-7558.
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