Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation

2026-08-19

AI Summary

研究通过Progressive Experience Fusion方法训练多任务TD-MPC2控制器，以解决自主血管内导航的长路径控制问题，显著提高了成功率。

Abstract

Autonomous endovascular navigation could support the delivery of mechanical thrombectomy to underserved areas, but controllers must navigate long, multi-stage paths across varying vascular anatomies. This study investigates Progressive Experience Fusion (PEF) to train a multi-task TD-MPC2 controller. We additionally evaluate a heuristic that changes the Model Predictive Path Integral planning horizon using residual action-sequence dispersion, and fine-tuning in a patient-specific simulation. Across five subtasks in ten known training anatomies with held-out targets, PEF achieved a mean success rate of 74%, compared with 37% for Soft Actor-Critic (p < 0.001) and 65% for base TD-MPC2 (p = 0.053). A PEF controller with adaptive-horizon planning trained on 30 vasculatures achieved a mean success rate of 90% in ten held-out vasculatures. The PEF agent successfully transferred to an unseen in vitro stroke patient vasculature under fluoroscopy, achieving a mean path ratio improvement from 63% to 80% with fine-tuning (p < 0.001), following 40x103 fine-tuning steps (corresponding to approximately 107 min of clinical inter-hospital transfer time). This work represents a proof of concept for multi-vasculature training and patient-specific adaptation, while further validation is required before clinical deployment.

Problem

• Autonomous endovascular navigation
• multi-vasculature training
• patient-specific adaptation

Innovation

• Progressive Experience Fusion
• TD-MPC2 controller
• autonomous endovascular navigation
• patient-specific adaptation
• model predictive path integral