GPC-based teleoperation for delay compensation and disturbance rejection in image-guided beating-heart surgery
Bibliographic record
Abstract
Beating-heart surgery is not currently possible for most surgical procedures as it requires superhuman skill to manually track the heart's motion while performing a surgical task. However, if a surgical tool could track the motion of the point of interest (POI) on the heart, then, with respect to the surgical tool tip the POI would appear stationary. Such a system can be created with a teleoperated surgical robot that is controlled to track the combination of the heart's and the surgeon's motion, as input through a separate user console. To develop such a system, the motion of the heart is found in ultrasound images where the image acquisition introduces delays of approximately 40 ms and image processing further increases this delay. Directly using this delayed position measurement in the feedback control loop can lead to instability and poor tracking. The generalized predictive controller used in this work compensates for this time delay despite large disturbances with velocities up to 210 mm/s and accelerations up to 3800 mm/s2caused by the moving heart.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".