CASIA OpenIR  > 多模态人工智能系统全国重点实验室
CASOG: Conservative Actor–Critic With SmOoth Gradient for Skill Learning in Robot-Assisted Intervention
Li, Hao1,2; Zhou, Xiao-Hu1,2; Xie, Xiao-Liang1,2; Liu, Shi-Qi1,2; Feng, Zhen-Qiu1,2; Hou, Zeng-Guang1,3
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN0278-0046
2023-09-18
Pages10
Corresponding AuthorZhou, Xiao-Hu(xiaohu.zhou@ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
AbstractThe robot-assisted intervention has shown reduced radiation exposure to physicians and improved precision in clinical trials. However, existing vascular robotic systems follow master-slave control mode and entirely rely on manual commands. This article proposes a novel offline reinforcement learning algorithm, Conservative Actor-critic with SmOoth Gradient (CASOG), to learn manipulation skills on vascular robotic systems. The proposed algorithm conservatively estimates Q-function and smooths gradients of convolution layers to deal with distribution shift and overfitting issues. Furthermore, to focus on complex manipulations, transitions with larger absolute temporal-difference error are sampled with higher probability. Comparative experiments on multiple vascular models and offline data demonstrate that CASOG delivers guidewire to the target with higher success rates and fewer backward steps than prior offline reinforcement learning methods. These results indicate that the proposed algorithm is promising to improve the autonomy of vascular robotic systems.
KeywordDeep neural network offline reinforcement learning robot-assisted intervention vascular robotic system
DOI10.1109/TIE.2023.3310021
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:001122255700001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55031
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorZhou, Xiao-Hu; Hou, Zeng-Guang
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Macau Univ Sci & Technol, Inst Syst Engn, CASIA MUST Joint Lab Intelligence Sci & Technol, Macau, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Hao,Zhou, Xiao-Hu,Xie, Xiao-Liang,et al. CASOG: Conservative Actor–Critic With SmOoth Gradient for Skill Learning in Robot-Assisted Intervention[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2023:10.
APA Li, Hao,Zhou, Xiao-Hu,Xie, Xiao-Liang,Liu, Shi-Qi,Feng, Zhen-Qiu,&Hou, Zeng-Guang.(2023).CASOG: Conservative Actor–Critic With SmOoth Gradient for Skill Learning in Robot-Assisted Intervention.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,10.
MLA Li, Hao,et al."CASOG: Conservative Actor–Critic With SmOoth Gradient for Skill Learning in Robot-Assisted Intervention".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023):10.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Hao]'s Articles
[Zhou, Xiao-Hu]'s Articles
[Xie, Xiao-Liang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Hao]'s Articles
[Zhou, Xiao-Hu]'s Articles
[Xie, Xiao-Liang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Hao]'s Articles
[Zhou, Xiao-Hu]'s Articles
[Xie, Xiao-Liang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.