Source note
Toward Safe Autonomous Robotic Endovascular Interventions using World Models
Summary
This paper studies autonomous robot navigation for mechanical thrombectomy and tests whether a world-model RL agent can handle longer, harder vascular navigation tasks across unseen anatomies. The main claim is that TD-MPC2 improves generalization and navigation quality over SAC in simulation, while keeping guidewire forces far below a stated rupture threshold and showing comparable in vitro success.
Problem
- The task is autonomous endovascular navigation for mechanical thrombectomy, where a robot must steer catheter-wire devices through patient-specific blood vessels in real time.
- Existing RL methods have trouble with long-horizon control, anatomy variation, and transfer beyond the vessels seen in training.
- Safety matters because excess tip force can damage vessels; the paper cites a proposed vessel rupture threshold of 1.5 N.
Approach
- The authors train a multi-task world-model agent based on TD-MPC2 and compare it with a state-of-the-art SAC baseline for five thrombectomy-relevant navigation tasks.
- The agent observes only tracked 2D device-tip state, previous state, target location, and previous action, which matches fluoroscopy-style guidance rather than full vessel geometry.
- Training uses 10 patient-specific vascular anatomies in simulation, with evaluation on 5 held-out anatomies plus fluoroscopy-guided in vitro tests on a patient-specific 3D-printed phantom.
- TD-MPC2 learns a latent dynamics model of how the catheter and wire move, then plans short action sequences inside that learned model instead of reacting step by step only from trial-and-error.
- The study also measures safety with mean and max guidewire tip contact force in simulation and applies sim-to-real augmentation by random scaling, rotation, and randomized insertion points during training.
Results
- Held-out simulation: TD-MPC2 reached 58% mean success rate vs 36% for SAC across tasks (p < 0.001), and 49% mean path ratio vs 22% (p < 0.001), but took longer: 17.1 s vs 9.6 s (p < 0.001).
- Safety in simulation: TD-MPC2 had 0.15 N mean tip force and 0.55 N max tip force, compared with 0.13 N and 0.50 N for SAC. These forces stayed well below the paper's 1.5 N rupture threshold.
- Task-level simulation gains: TD-MPC2 improved success on hard tasks such as A2L: 38% vs 8%, A2R: 52% vs 8%, and A3L: 38% vs 16%; corresponding path-ratio gains were 40% vs 21%, 48% vs 24%, and 63% vs 21%.
- In vitro phantom tests: TD-MPC2 achieved 68% mean success vs 60% for SAC with no significant difference (p = 0.567), but had better mean path ratio 54% vs 44% (p < 0.05) and slower procedures: 111.0 s vs 66.2 s (p < 0.001).
- In vitro task detail: On A2L, both methods had 0% success, but TD-MPC2 improved path ratio to 71% vs 47% (p = 0.017). On A2R, TD-MPC2 had 60% success vs 40% for SAC; on A3R, 80% vs 60%, though these were not significant in the reported small-sample setting.
- Training efficiency: Reported exploration steps for the shown results were 0.5e6 for TD-MPC2 after 25 hours and 4.5e6 for SAC after 75 hours, suggesting faster learning for the world-model setup at the reported operating point.