Autonomous Blood Suction for Robot-Assisted Surgery: A Sim-to-Real Reinforcement Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
Recent applications of deep reinforcement learning (DRL) in surgical autonomy have shown promising results in automating various surgical sub-tasks. While most of these studies consider the rigid and soft body dynamics in the surgery such as tissue deformation, only a few have investigated the situation where fluid is present. However, the presence of fluids, particularly blood, is common in surgeries, and interacting with them adds additional challenges to task automation. In this work, we investigate the use of DRL in automating blood suction, a common surgical sub-task where blood is removed from the surgical field. We build a blood suction simulation environment based on position-based fluids (PBF), train an agent with domain-randomized environment parameters through curriculum learning, and obtain a generalizable policy that can be applied to various shapes of tissue and types of liquid. Real-world experiments show that the agent can perform autonomous suction in different tissue models with different amounts and types of liquid, and only one of the 50 trials resulted in more than 3 ml of blood remaining.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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