Skill learning approach based on impedance control for spine surgical training simulators with haptic playback
Bibliographic record
Abstract
In this article, a robust adaptive impedance control based on the modified function approximation technique, and augmented with a novel integral nonsingular terminal sliding mode control, is proposed for a surgical haptic system. The haptic device is maneuvered by surgeons, whereby the mode of action, motion path, and haptic dynamics are rendered as unknowns. The inclusion of integral nonsingular terminal sliding mode control leverages the capability of ensuring consistent tracking of system’s trajectories performance, fast transient response, finite-time convergence, improved robustness, and reduced chattering. However, the modified function approximation technique strategy allows to estimate model’s dynamics regardless of the availability of dynamic uncertainties’ lower and upper bounds. In addition, an estimate of the intended motion mode and path is integrated in the evolved adaptive impedance control, via radial basis function neural network, permitting the haptic arm to track the target impedance model. Finally, using the haptic arm, controlled experimental cases and comparative study were done, after which the proposed surgical simulator adopted the results for real-time validation.
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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 | 0.000 |
| 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".