Toward Safe Autonomous Robotic Endovascular Interventions using World Models
Autonomous mechanical thrombectomy (MT) presents substantial challenges due to highly variable vascular geometries and the requirements for accurate, real-time control. While reinforcement learning (RL) has emerged as a promising paradigm for the automation of endovascular navigation, existing approaches often show limited robustness when faced with diverse patient anatomies or extended navigation horizons. In this work, we investigate a world-model-based framework for autonomous endovascular navigation built on TD-MPC2, a model-based RL method that integrates planning and learned dynamics. We evaluate a TD-MPC2 agent trained on multiple navigation tasks across hold out patient-specific v...
| Authors | Harry Robertshaw, Nikola Fischer, Han-Ru Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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