Robotic Assistance and Haptic Feedback in Arthroscopic Procedures: Design and Preliminary Evaluation of a Two-Arm System
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Bibliographic record
Abstract
Robot-assisted arthroscopic surgery has been receiving growing attention in the field of orthopedic surgery. Most of the existing robot-assisted surgical systems in orthopedics place more focus on open surgery than minimally invasive surgery (MIS). In traditional arthroscopic surgery, the surgeon needs to hold an arthroscope with one hand while performing the surgical operations with the other hand, which can restrict the dexterity of the surgical performance and increase the cognitive load. On the other hand, the surgeon heavily relies on the arthroscope view when conducting the surgery, whereas the arthroscope view is a largely localized view and lacks depth information. To assist the surgeon in both scenarios, in this work, we develop a two-arm robotic system. The left-arm robot is used as a robot-assisted arthroscope holder, and it can hold the arthroscope still at a designated pose and reject all other potential disturbances, while also allowing the operator to move it via a pedal switch whenever needed. The left-arm robot is implemented with an impedance controller and a gravity iterative learning (Git) scheme, where the former can provide compliant robot behavior, thus ensuring a safe human–robot interaction, while the latter can accurately learn for gravity compensation. The right-arm robot is used as a robot-assisted surgical tool, providing virtual fixture (VF) assistance and haptic feedback during the surgery. The right-arm robot is implemented with a point-based VF algorithm, which can generate VF directly from point clouds in any shape, render force feedback, and deliver it to the operator. Furthermore, the VF, the bone, and the surgical tool position are visualized in a 3D digital environment as additional visual feedback for the operator. A series of experiments are conducted to evaluate the effectiveness of the prototype. The results demonstrate that both arms can provide satisfactory assistance as designed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it