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Discrete soft actor-critic with auto-encoder on vascular robotic system

文献类型：期刊论文

作者Li, Hao3,4; Zhou, Xiao-Hu3,4; Xie, Xiao-Liang3,4; Liu, Shi-Qi3,4; Gui, Mei-Jiang3,4; Xiang, Tian-Yu3,4; Wang, Jin-Li2; Hou, Zeng-Guang1,3,4
刊名ROBOTICA
出版日期2022-11-17
页码12
ISSN号0263-5747
关键词surgical robots vascular robotic system automation reinforcement learning deep neural network
DOI10.1017/S0263574722001527
通讯作者Zhou, Xiao-Hu(xiaohu.zhou@ia.ac.cn) ; Xie, Xiao-Liang(xiaoliang.xie@ia.ac.cn)
英文摘要Instrument delivery is critical part in vascular intervention surgery. Due to the soft-body structure of instruments, the relationship between manipulation commands and instrument motion is non-linear, making instrument delivery challenging and time-consuming. Reinforcement learning has the potential to learn manipulation skills and automate instrument delivery with enhanced success rates and reduced workload of physicians. However, due to the sample inefficiency when using high-dimensional images, existing reinforcement learning algorithms are limited on realistic vascular robotic systems. To alleviate this problem, this paper proposes discrete soft actor-critic with auto-encoder (DSAC-AE) that augments SAC-discrete with an auxiliary reconstruction task. The algorithm is applied with distributed sample collection and parameter update in a robot-assisted preclinical environment. Experimental results indicate that guidewire delivery can be automatically implemented after 50k sampling steps in less than 15 h, demonstrating the proposed algorithm has the great potential to learn manipulation skill for vascular robotic systems.
WOS关键词INTERVENTIONAL CARDIOLOGISTS ; STAFF ; RISK
资助项目National Natural Science Foundation of China[62003343] ; National Natural Science Foundation of China[62222316] ; National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[62073325] ; National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[U20A20224] ; National Natural Science Foundation of China[U1913210] ; Beijing Natural Science Foundation[M22008] ; Youth Innovation Promotion Association of ChineseAcademy of Sciences (CAS)[2020140] ; Strategic Priority Research Program of CAS[XDB32040000]
WOS研究方向Robotics
语种英语
出版者CAMBRIDGE UNIV PRESS
WOS记录号WOS:000889895900001
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association of ChineseAcademy of Sciences (CAS) ; Strategic Priority Research Program of CAS
源URL[http://ir.ia.ac.cn/handle/173211/50797]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Zhou, Xiao-Hu; Xie, Xiao-Liang
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
2.China Univ Min & Technol, Sch Mech Elect & Informat Engn, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Hao,Zhou, Xiao-Hu,Xie, Xiao-Liang,et al. Discrete soft actor-critic with auto-encoder on vascular robotic system[J]. ROBOTICA,2022:12.
APA Li, Hao.,Zhou, Xiao-Hu.,Xie, Xiao-Liang.,Liu, Shi-Qi.,Gui, Mei-Jiang.,...&Hou, Zeng-Guang.(2022).Discrete soft actor-critic with auto-encoder on vascular robotic system.ROBOTICA,12.
MLA Li, Hao,et al."Discrete soft actor-critic with auto-encoder on vascular robotic system".ROBOTICA (2022):12.

入库方式： OAI收割

来源：自动化研究所

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