Learning Expert Strategy for Autonomous Robotic Endovascular Intervention via Decoupled Procedural Execution

2026-06-30

AI Summary

Autonomous endovascular interventions face challenges in simultaneously ensuring safety and procedural consistency due to the tight coupling between decision-making and execution. This work proposes a decoupled framework wherein a high-level reinforcement learning agent generates global navigation policies, while a low-level execution module—explicitly embedding clinical protocols, kinematic constraints, and vascular safety requirements through expert knowledge—translates these policies into actions. By directly integrating expert-derived guidelines into the execution layer, the approach enhances procedural standardization without compromising safety. Experimental results demonstrate that the system achieves over 96% navigation success rates in both simulated and real robotic platforms, reduces the number of intervention steps by 29.3%, and decreases trajectory variance by 13%, thereby significantly improving both efficiency and consistency.

Abstract

Endovascular interventions are high-stakes procedures requiring precise device operation within complex and tortuous vascular anatomies. Autonomous endovascular navigation has the potential to standardize procedural quality and reduce the performance variability inherent in manual operation. Although Reinforcement Learning (RL) approaches have demonstrated promise in enabling autonomy in endovascular intervention, they often struggle with explicit constraint satisfaction and safety guarantees. To address these challenges, a learning-based expert strategy is introduced, enhancing procedural consistency in autonomous endovascular intervention by explicitly decoupling high-level strategic decision-making from low-level procedural execution. The proposed framework replicates the expert clinical decision-making process: a strategic RL policy generates global navigation intents, which are subsequently refined through an expert-informed execution module. This module ensures that robot movements strictly adhere to expert operational norms, real-time kinematic limits, and vessel safety constraints. Experimental evaluation across high-fidelity 3D simulations and a real-world robotic platform demonstrates that the proposed framework not only outperforms baseline policies but also effectively replicates expert-level proficiency. The framework achieves a high navigation success rate (> 96%) and a 29.3% reduction in operational steps, which translates to enhanced operative efficiency and minimized device-vessel interaction. Furthermore, a 13% reduction in trajectory variance indicates superior procedural standardization, aligning autonomous behavior with established clinical norms. These results underscore its potential to enhance the predictability, safety, and consistency of robotic endovascular interventions.

Problem

• Autonomous Endovascular Intervention
• Constraint Satisfaction
• Safety Guarantees
• Procedural Consistency
• Expert Strategy

Innovation

• Decoupled Procedural Execution
• Reinforcement Learning
• Expert Strategy
• Autonomous Endovascular Intervention
• Safety Constraints