Autonomous Blood Suction for Robot-Assisted Surgery: A Sim-to-Real Reinforcement Learning Approach
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".