[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84446-en":3,"doc-seo-84446-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84446,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782109480056885918",8,"Research & Report","Towards Autonomous Soft Robotic Endovascular Navigation via Imitation Learning","Endovascular interventionists advance catheters and guidewires through patient vasculature under fluoroscopy, where soft robotic tools promise improved maneuverability but remain difficult to model, control, and sense. This work proposes an autonomous soft robotic guidewire navigation approach using transformer-based imitation learning with goal conditioning, relative action outputs, and automatic contrast dye injections. A large-scale 2D-projected environment generates 647 simulated fluoroscopy demonstrations across 36 geometries, achieving 83% success on unseen vascular structures and 75% on an unseen patient-derived geometry, validated via ablations and baselines.","Towards Autonomous Soft Robotic Endovascular Navigation via  \nImitation Learning  \nNoah Barnes 1 , Ji Woong Kim2 , Lingyun Di3 , Hannah Qu 1 , Anuruddha Bhattacharjee 1 , Miroslaw Janowski4 , Dheeraj Gandhi4 , Bailey Felix6 , Shaopeng Jiang5 , Olivia Young6 , Mark Fuge7 , Ryan D. Sochol6 ,  \nJeremy D. Brown 1 , and Axel Krieger1  \nThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice,  \nafter which this version may no longer be accessible.  \narXiv :2510 .09497v2 [ cs .RO] 11 Jul 2026  \nAbstract—In endovascular surgery, endovascular interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient’s blood vessels to treat various conditions such as blood clots, aneurysms, and malformations. Robotic guidewires can enhance maneuverability but are difficult to model and control. Autonomous soft robotic guidewire navigation has the potential to overcome these challenges, increasing the precision and safety of endovascular navigation. As a first step, we establish a large-scale, 2D-projected environment for autonomous navigation. In other surgical domains, end-to-end imitation learning has shown promising results. Thus, we develop a transformer-based imitation learning framework with goal conditioning, relative action outputs, and automatic contrast dye injections to enable generalizable soft robot navigation in an aneurysm targeting task. We train the policy on 36 different modular bifurcated geometries, generating 647 total demonstrations under simulated fluoroscopy, and evaluate it on three previously unseen vascular geometries. The policy reaches the aneurysm with a success rate of 83% on the unseen geometries, outperforming several baselines. In addition, ablation and baseline studies evaluate the effectiveness of each design and data collection choice. Lastly, we extend the policy to achieve 75% success on an unseen patient-derived geometry. Project website: [https://softrobotnavigation.github.io/](https://softrobotnavigation.github.io/)  \nIndex Terms—Soft robot, imitation learning, endovascular surgery, autonomous navigation  \nI. INTRODUCTION  \nDIAGNOSIS and treatment of vascular conditions require  \nan endovascular interventionist to skillfully advance catheters and guidewires through the patient’s blood vessels. Robotically steerable tools can improve maneuverability over conventional tools [2] . However, complex vessel-tool forces  \n1Johns Hopkins University, {nbarne18, hqu6, abhatt27, jdelainebrown, [axel](axel}@jhu.edu)[}](axel}@jhu.edu)[@jhu.edu](axel}@jhu.edu)  \n2 Stanford University, [jwbkim@stanford.edu](jwbkim@stanford.edu)  \n3 McGill University, [lingyun.di@mail.mcgill.ca](lingyun.di@mail.mcgill.ca)  \n4University of Maryland, Baltimore, {miroslaw.janowski, [dheeraj.gandhi](dheeraj.gandhi}@som.umaryland.edu)[}](dheeraj.gandhi}@som.umaryland.edu)[@som.umaryland.edu](dheeraj.gandhi}@som.umaryland.edu)  \n5 Swiss Federal Institute of Technology in Lausanne (EPFL), [shaopeng.jiang@epfl.ch](shaopeng.jiang@epfl.ch)  \n6University of Maryland, College Park, {bmfelix, oyoung, rso[chol](chol}@umd.edu)[}](chol}@umd.edu)[@umd.edu](chol}@umd.edu)  \n7ETH Zurich, mafuge@ethz.ch  \nThis work was supported in part by National Institutes of Health R01EB033354 . In addition, the work was supported in part by the Maryland Robotics Center and the Center for Engineering Concepts Development at the University of Maryland. Finally, this material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 2236417 and 2139757 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  \nSmall-scale tools  Large-scale tool  \nFig. 1. (Top) Commercial guidewire and microcatheter for neurovascular intervention next to a soft robotic microcatheter [1] and ","cbCaijdbsLBMyiIt","https://ap.wps.com/l/cbCaijdbsLBMyiIt","pdf",2610581,1,9,"English","en",105,"# Introduction\n## Background and Motivation\n## Proposed Approach","[{\"question\":\"What problem does the document address in endovascular surgery?\",\"answer\":\"It addresses how interventionists navigate and control thin, highly flexible tools inside blood vessels where forces, sensing, and vision constraints make consistent control difficult.\"},{\"question\":\"What learning framework is proposed for autonomous navigation?\",\"answer\":\"A transformer-based imitation learning framework is proposed with goal conditioning, relative action outputs, and automatic contrast dye injections to enable generalizable navigation.\"},{\"question\":\"How is the policy trained and evaluated?\",\"answer\":\"The policy is trained on demonstrations collected from 36 modular bifurcated geometries using simulated fluoroscopy, then evaluated on three previously unseen vascular geometries and one unseen patient-derived geometry.\"}]",1784195668,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"towards-autonomous-soft-robotic-endovascular-navigation-via-imitation-learning","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/towards-autonomous-soft-robotic-endovascular-navigation-via-imitation-learning/84446/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address in endovascular surgery?","Question",{"text":75,"@type":76},"It addresses how interventionists navigate and control thin, highly flexible tools inside blood vessels where forces, sensing, and vision constraints make consistent control difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What learning framework is proposed for autonomous navigation?",{"text":80,"@type":76},"A transformer-based imitation learning framework is proposed with goal conditioning, relative action outputs, and automatic contrast dye injections to enable generalizable navigation.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the policy trained and evaluated?",{"text":84,"@type":76},"The policy is trained on demonstrations collected from 36 modular bifurcated geometries using simulated fluoroscopy, then evaluated on three previously unseen vascular geometries and one unseen patient-derived geometry.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]