VAISI: Vision-Based Adaptive Impedance-Control for Surgical Incisions
Bibliographic record
Abstract
Robot-assisted surgery requires precise control of both position and interaction forces during soft tissue manipulation, a task challenged by the non-linear and highly variable mechanical properties of soft tissues. Skin incisions are a critical first step in many surgical procedures, and performing them accurately presents a fundamental robotic challenge in soft material manipulation. This paper introduces VAISI (Vision-Based Adaptive Impedance-Control for Surgical Incisions), a novel model-free robotic control framework that leverages real-time stereo vision feedback for precise depth regulation and force modulation during skin incisions. This approach is coupled with a compact scalpel-camera end-effector to measure the state and deformations of the targeted soft tissue. The VAISI framework uses vision-based feedback to adapt end-effector stiffness and trajectory via Cartesian impedance control, minimizing excess force while achieving accurate incisions. Experimental validation on ex vivo porcine belly and hock skin demonstrates that a low-constant-stiffness approach fails to apply enough force to create incisions, whereas VAISI enables sub-millimeter depth-accurate cuts with a maximum standard deviation of 1.23 mm, emphasizing the necessity of force adaptation in unknown situations. These results highlight the ability of vision-guided adaptive control for safe and precise soft tissue incisions in future autonomous surgical systems.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".