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CASOG: Conservative Actor–Critic With SmOoth Gradient for Skill Learning in Robot-Assisted Intervention

文献类型：期刊论文

作者Li, Hao1,2; Zhou, Xiao-Hu1,2; Xie, Xiao-Liang1,2; Liu, Shi-Qi1,2; Feng, Zhen-Qiu1,2; Hou, Zeng-Guang1,3
刊名IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
出版日期2023-09-18
页码10
ISSN号0278-0046
关键词Deep neural network offline reinforcement learning robot-assisted intervention vascular robotic system
DOI10.1109/TIE.2023.3310021
通讯作者Zhou, Xiao-Hu(xiaohu.zhou@ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
英文摘要The 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.
资助项目National Natural Science Foundation of China
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001122255700001
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/55030]  
专题多模态人工智能系统全国重点实验室
通讯作者Zhou, Xiao-Hu; Hou, Zeng-Guang
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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