Autonomous Soft Robotic Guidewire Navigation via Imitation Learning

• URL: http://arxiv.org/abs/2510.09497v1
• Date: Fri, 10 Oct 2025 15:57:09 GMT
• Title: Autonomous Soft Robotic Guidewire Navigation via Imitation Learning
• Authors: Noah Barnes, Ji Woong Kim, Lingyun Di, Hannah Qu, Anuruddha Bhattacharjee, Miroslaw Janowski, Dheeraj Gandhi, Bailey Felix, Shaopeng Jiang, Olivia Young, Mark Fuge, Ryan D. Sochol, Jeremy D. Brown, Axel Krieger,
• Abstract summary: In endovascular surgery, interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels.<n>Guidewires with robotic tips can enhance maneuverability, but they present challenges in modeling and control.<n>We develop a transformer-based imitation learning framework with goal conditioning, relative action outputs, and automatic contrast dye injections.
• Score: 3.1381624795986345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Guidewires with robotic tips can enhance maneuverability, but they present challenges in modeling and control. Automation of soft robotic guidewire navigation has the potential to overcome these challenges, increasing the precision and safety of endovascular 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 robotic guidewire navigation in an aneurysm targeting task. We train the model on 36 different modular bifurcated geometries, generating 647 total demonstrations under simulated fluoroscopy, and evaluate it on three previously unseen vascular geometries. The model can autonomously drive the tip of the robot to the aneurysm location with a success rate of 83% on the unseen geometries, outperforming several baselines. In addition, we present ablation and baseline studies to evaluate the effectiveness of each design and data collection choice. Project website: https://softrobotnavigation.github.io/

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