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[2602.18663] Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL)
Summary
This article presents a novel Hierarchical Modular Multi-Agent Reinforcement Learning (HM-MARL) framework aimed at enhancing autonomous navigation for mechanical thrombectomy, demonstrating significant success rates in both simulated and real-world environments.
Why It Matters
The research addresses critical challenges in mechanical thrombectomy, a vital procedure for treating strokes. By improving autonomous navigation using advanced AI techniques, this work could enhance patient outcomes and accessibility to life-saving treatments, especially in areas with limited medical resources.
Key Takeaways
- The HM-MARL framework enables efficient navigation in thrombectomy procedures.
- In silico models achieved high success rates, indicating strong potential for real-world applications.
- Challenges remain in the simulation-to-real transition, necessitating further refinement of RL strategies.
Computer Science > Robotics arXiv:2602.18663 (cs) [Submitted on 20 Feb 2026] Title:Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL) Authors:Harry Robertshaw, Nikola Fischer, Lennart Karstensen, Benjamin Jackson, Xingyu Chen, S.M.Hadi Sadati, Christos Bergeles, Alejandro Granados, Thomas C Booth View a PDF of the paper titled Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL), by Harry Robertshaw and 8 other authors View PDF HTML (experimental) Abstract:Mechanical thrombectomy (MT) is typically the optimal treatment for acute ischemic stroke involving large vessel occlusions, but access is limited due to geographic and logistical barriers. Reinforcement learning (RL) shows promise in autonomous endovascular navigation, but generalization across 'long' navigation tasks remains challenging. We propose a Hierarchical Modular Multi-Agent Reinforcement Learning (HM-MARL) framework for autonomous two-device navigation in vitro, enabling efficient and generalizable navigation. HM-MARL was developed to autonomously navigate a guide catheter and guidewire from the femoral artery to the internal carotid artery (ICA). A modular multi-agent approach was used to decompose the complex navigation task into specialized subtasks, each trained using Soft Actor-Critic RL. The framework was validated in both in silico and in vitro t...