Robust Control of Collaborative Dual-User Haptic Training System: An Autonomous Variable Impedance Scheme
Bibliographic record
Abstract
Lack of adequate skills are the most prominent contributor to the persistent problem of surgical errors throughout the early phases of a novice surgeon's training. Therefore, it is essential to thoughtfully and methodically consider the development of an effective strategy for transferring the trainers' expertise to novice surgeons, while simultaneously increasing the trainers' involvement in the training process. This article proposes a collaborative dual-user haptic-enabled surgical training system that utilizes a responsive variable impedance control structure. In this training system, a novice (trainee) and an expert (trainer) collaboratively conduct a particular task through their respective haptic devices. The desired parameters of the impedance model for the trainer's haptic device remain constant throughout the operation, while those of the trainee's haptic device are time-varying depending on his/her relative task performance level. The main purpose of this performance-based variable impedance control structure is to imitate or enhance the hands-on training experience. High-gain observers with unknown input are considered to estimate the interaction forces. The small-gain theorem and the input-to-state stability method are utilized to examine the overall nonlinear closed-loop system stability. Experiments are conducted to demonstrate the effectiveness of the proposed training system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".