Caging a novel object using multi-task learning method
Su, Jianhua1; Chen, Bin2; Qiao, Hong1; Liu, Zhi-yong1
发表期刊NEUROCOMPUTING
ISSN0925-2312
2019-07-25
卷号351页码:146-155
通讯作者Su, Jianhua(jianhua.su@ia.ac.cn)
摘要Caging 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.
关键词Multi-task learning Grasping Kernel regression
DOI10.1016/j.neucom.2019.03.063
关键词[WOS]REGRESSION
收录类别SCI
语种英语
资助项目Science and Technology on Space Intelligent Control Laboratory[HTKJ2019KL502013] ; NSFC[91848109] ; Beijing Natural Science Foundation[4182068] ; NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization[U1509212] ; NSFC-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]
项目资助者NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization ; Beijing Natural Science Foundation ; NSFC ; Science and Technology on Space Intelligent Control Laboratory
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000467803400015
出版者ELSEVIER SCIENCE BV
七大方向——子方向分类机器人感知与决策
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/24218
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Su, Jianhua
作者单位1.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
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
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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