Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access

Harry Robertshaw; Weijie Qi; Nikola Fischer; Alejandro Granados; Thomas C. Booth; Sam E. John

cs.ROOct 2, 2026

Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access

Authors: Harry Robertshaw, Weijie Qi, Nikola Fischer, Alejandro Granados, Thomas C. Booth, Sam E. John

Organizations: School of Biomedical Engineering & Imaging Sciences, Kings College London, London, UK · Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia · Department of Neuroradiology, King’s College Hospital, London, UK

Abstract

Endovascular brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This work presents the first demonstration of in vitro autonomous robotic navigation for endovascular BCI access in the cerebral venous system. Soft Actor-Critic controllers were trained in silico for two sequential tasks spanning the right internal jugular vein to the superior sagittal sinus, using geometric augmentation of one training anatomy. Navigation was evaluated in a training anatomy and an anatomically unseen hold-out model over 250 in silico episodes and five fluoroscopy-guided in vitro robotic runs per task-anatomy condition, comprising 1,000 simulated episodes and 20 physical runs overall. Task recurrent predictors were also evaluated for online identification of impending navigation failure. In silico success rates for Tasks A and B were 85.6% and 98.4% in the training anatomy and 42.0% and 91.6% in the hold-out anatomy, respectively. Fourteen of 20 physical runs were successful (70% overall), including 80% success for Task B in the hold-out phantom. In silico the predictors detected 99.3-100.0% of failures with false-alarm rates of 0.8-6.7%. During in vitro evaluation, predicted risk increased before failed episodes, but elevated probabilities during some successful runs showed reduced calibration after transfer. These results demonstrate the feasibility of autonomous cerebral venous access and show how online failure prediction could support human oversight, while also identifying anatomical generalization and sim-to-real calibration as priorities before preclinical translation.

Figures & tables

Figure 1: Labeled anatomy for navigation tasks A and B, with task start and target region highlighted.

Anatomy	Task	Length (mm)	Tortuosity
Training	A	224.6	1.72
Training	B	145.4	1.49
Hold-out	A	211.9	1.73
Hold-out	B	161.3	1.39

Table 1: Centreline length and tortuosity for each anatomy and task. Length was calculated as the sum of the Euclidean distances between consecutive centreline points. Tortuosity was calculated as the centreline length divided by the straight-line distance between the first and last centreline points; a value of 1 indicates a straight path.

Figure 2: Labeled in vitro testbed, with inset of 3D vascular phantom and labeled anatomy.

Figure 3: Robotic manipulator used for in vitro evaluation.

Figure 4: Failure predictor architecture, comprising the recurrent input sequence, 128-unit GRU, prediction head, and sigmoid output estimating failure probability within the next $H=20$ control steps.

Parameter	Value
Learning rate	$2.20\times 10^{-4}$
Discount factor ( $\gamma$ )	0.99
Batch size	32
Replay buffer size	$10^{4}$
Training steps	$2\times 10^{7}$
Heat-up steps	$5\times 10^{5}$

Table 2: In silico training hyperparameters.

Figure 5: Phantom vessel model under fluoroscopy, showing start and target regions for task A and B.

Anatomy	Task	Success rate (%, 95% CI)	Path ratio (%)	Procedure time (s)
Training	A	85.6 (214/250, 80.7–89.4%)	13.7 $\pm$ 23.2	10.33 $\pm$ 4.60
Training	B	98.4 (246/250, 96.0–99.4%)	31.7 $\pm$ 4.5	13.40 $\pm$ 5.64
Hold-out	A	42.0 (105/250, 36.0–48.2%)	8.4 $\pm$ 1.4	12.15 $\pm$ 6.44
Hold-out	B	91.6 (229/250, 87.5–94.4%)	30.2 $\pm$ 19.3	11.77 $\pm$ 5.74

Table 3: In silico navigation performance across anatomy and task conditions. Values are reported over 250 episodes per condition. Path ratio is summarized over unsuccessful episodes, and procedure time over successful episodes. Continuous values are mean

\pm

SD.

Anatomy	Task	Detection (%, 95% CI)	Lead time (steps)	False alarm (%, 95% CI)	Brier score
Training	A	100.0 (36/36, 90.4–100.0%)	23.0 [21.8, 24.0]	2.3 (5/214, 1.0–5.4%)	$0.0032\pm 0.0122$
Training	B	100.0 (4/4, 51.0–100.0%)	22.0 [21.5, 22.5]	0.8 (2/246, 0.2–2.9%)	$0.0003\pm 0.0018$
Hold-out	A	99.3 (144/145, 96.2–99.9%)	27.0 [26.0, 28.0]	6.7 (7/105, 3.3–13.1%)	$0.0203\pm 0.0212$
Hold-out	B	100.0 (21/21, 84.5–100.0%)	22.0 [20.0, 29.0]	0.9 (2/229, 0.2–3.1%)	$0.0026\pm 0.0103$

Table 4: Failure-predictor performance in silico . Proportion confidence intervals are Wilson 95% CIs. Warning lead time is median [IQR], and Brier score is mean

\pm

SD.

Figure 6: Temporal failure-prediction performance, across training and hold-out anatomies. Mean predictor outputs are shown for unsuccessful episodes aligned to failure (left) and successful episodes aligned to completion (right). Time is normalized to episode duration. Solid and dashed curves are in silico and in vitro episodes, respectively; shaded regions show 95% CIs.

Anatomy	Task	Success rate (%)	Path ratio (%)	Procedure time (s)
Training	A	80 (4/5)	$84$ ( $n=1$ )	$188\pm 96$
Training	B	80 (4/5)	$35$ ( $n=1$ )	$85\pm 29$
Hold-out	A	40 (2/5)	$88\pm 10$	$101\pm 33$
Hold-out	B	80 (4/5)	$56$ ( $n=1$ )	$93\pm 56$

Table 5: In vitro navigation performance across anatomy and task conditions. Each condition comprised five runs. Path ratio is summarized over unsuccessful runs, and procedure time over successful runs. Continuous values are mean

\pm

SD.

Figure 7: Spatial distribution of predictor-triggered guidewire-retraction onsets during armed navigation in the hold-out in vitro anatomy. Heatmap intensity indicates the relative frequency of retraction onset.

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Toward Safe Autonomous Robotic Endovascular Interventions using World Models

Harry Robertshaw, Nikola Fischer, Han-Ru Wu, Andrea Walker Perez+5

Surgical & Interventional Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, UK · Department of Radiology, National Taiwan University Hospital, Taiwan · Department of Neuroradiology, King’s College Hospital, UK

World Models for Robotics Robotic Endovascular Intervention Surgical Robotics Model-Based RL

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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 vasculatures and benchmark its performance against the state-of-the-art Soft Actor-Critic (SAC) algorithm agent. Both approaches are further validated in vitro using patient-specific vascular phantoms under fluoroscopic guidance. In simulation, TD-MPC2 demonstrates a significantly higher mean success rate than SAC (58% vs. 36%, p < 0.001), and mean tip contact forces of 0.15 N, well below the proposed 1.5 N vessel rupture threshold. Mean success rates for TD-MPC2 (68%) were comparable to SAC (60%) in vitro, but TD-MPC2 achieved superior path ratios (p = 0.017) at the cost of longer procedure times (p < 0.001). Together, these results provide the first demonstration of autonomous MT navigation validated across both hold out in silico data and fluoroscopy-guided in vitro experiments, highlighting the promise of world models for safe and generalizable AI-assisted endovascular interventions.

Abstract

Robotic Endovascular Intervention

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