Robot-Assisted Vascular Shunt Insertion with the dVRK Surgical Robot
Why this work is in the frame
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Bibliographic record
Abstract
Vascular shunt insertion is a common surgical procedure performed to restore blood flow to damaged tissues temporarily. It usually requires a surgeon and a surgical assistant. We consider three scenarios: (1) a surgeon is available locally; (2) a remote surgeon is available via teleoperation; (3) no surgeon is available. In each scenario, a minimally invasive surgical-assistant da Vinci robot operates in a different mode either by teleoperation or automation. Robotic assistance for this procedure is challenging due to precision and control uncertainty. The role of the robot in this task depends on the availability of a human surgeon. We propose a trimodal framework for vascular shunt insertion assisted by a da Vinci Research Kit (dVRK) robotic surgical assistant (RSA). To help further study for the community, we also present a physics-based simulated environment for shunt insertion built on top of the NVIDIA Isaac ORBIT simulator. We collect a large dataset of trajectories for the shunt insertion environment using ORBIT and implement these trajectories to show the simulator’s realism, showcasing the possibility for future work to use the simulator for policy learning. Physical experiments demonstrate a success rate of 65–100% for mode (1), 100% for mode (2), and 75–95% for mode (3) across vessel phantoms with different sizes, color, and material properties. For dataset and videos, see https://sites.google.com/berkeley.edu/ravsi .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it