CASIA OpenIR
Caging a novel object using multi-task learning method
Su, Jianhua1; Chen, Bin2; Qiao, Hong1; Liu, Zhi-yong1
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-07-25
Volume351Pages:146-155
Corresponding AuthorSu, Jianhua(jianhua.su@ia.ac.cn)
AbstractCaging grasps provide a way to manipulate an object without full immobilization and enable dealing with the pose uncertainties of the object. Most previous works have constructed caging sets by using the geometric models of the object. This work aims to present a learning-based method for caging a novel object only with its image. A caging set is first defined using the constrained region, and a mapping from the image feature to the caging set is then constructed with kernel regression function. Avoiding the collection of large number of samples, a multi-task learning method is developed to build the regression function, where several different caging tasks are trained with a joint model. In order to transfer the caging experience to a new caging task rapidly, shape similarity for caging knowledge transfer is utilized. Thus, given only the shape context for a novel object, the learner is able to accurately predict the caging set through zero-shot learning. The proposed method can be applied to the caging of a target object in a complex real-world environment, for which the user only needs to know the shape feature of the object, without the need for the geometric model. Several experiments prove the validity of our method. (C) 2019 Elsevier B.V. All rights reserved.
KeywordMulti-task learning Grasping Kernel regression
DOI10.1016/j.neucom.2019.03.063
WOS KeywordREGRESSION
Indexed BySCI
Language英语
Funding ProjectNSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization[U1509212] ; Beijing Natural Science Foundation[4182068] ; NSFC[91848109] ; Science and Technology on Space Intelligent Control Laboratory[HTKJ2019KL502013]
Funding OrganizationNSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization ; Beijing Natural Science Foundation ; NSFC ; Science and Technology on Space Intelligent Control Laboratory
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000467803400015
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24218
Collection中国科学院自动化研究所
Corresponding AuthorSu, Jianhua
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Su, Jianhua,Chen, Bin,Qiao, Hong,et al. Caging a novel object using multi-task learning method[J]. NEUROCOMPUTING,2019,351:146-155.
APA Su, Jianhua,Chen, Bin,Qiao, Hong,&Liu, Zhi-yong.(2019).Caging a novel object using multi-task learning method.NEUROCOMPUTING,351,146-155.
MLA Su, Jianhua,et al."Caging a novel object using multi-task learning method".NEUROCOMPUTING 351(2019):146-155.
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