ASAP: A Semi-Autonomous Precise System for Telesurgery During Communication Delays
Bibliographic record
Abstract
In remote, rural, and disadvantaged areas, telesurgery can be severely hindered by limitations of communication infrastructure. In conventional telesurgery, delays as small as 300ms can produce fatal surgical errors. To mitigate the effect of communication delays during telesurgery, we introduce a semi-autonomous system that decouples the user interaction from the robot execution. This system uses a physics-based simulator where a surgeon can demonstrate individual surgical subtasks, with immediate graphical feedback. Each subtask is performed asynchronously, unaffected by communication latency, jitter, and packet loss. A surgical step recognition module extracts the intended actions from the observed surgeon-simulation interaction. The remote robot can perform each one of these actions autonomously. The action recognition system leveraged a transfer learning approach that minimized the data needed during training, and most of the learning is obtained from simulated data. We tested this system in two tasks: fluid-submerged peg transfer (resembling bleeding events) and surgical debridement. The system showed robustness to delays of up to 5 seconds, maintaining a performance rate of 87% for peg transfer and 88% for debridement. Also, the framework reduced the completion time under delays by 45% and 11% during peg transfer and debridement, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".