Remote Robotic Palpation With Depth-Vision-Driven Autonomous-Dimensionality-Reduction Shared Control
Bibliographic record
Abstract
Teleoperated medical robots have the potential to revolutionize healthcare. However, when developing systems for tasks like remote palpation, state-of-the-art literature still uses test phantoms of oversimplified geometries, due to the complexity of the required mechanical robot–patient interaction. In reality, human bodies have complex 3-D shapes and require fine-tuning of all six manipulator's degrees of freedom, controlled by the user. In this article, we argue that the implementation of depth-vision-driven autonomous dimensionality-reduction (DVD ADR) shared control can greatly improve the users' performance. The proposed control method keeps the user in control of the end-effector’s position, while automatically adjusting its orientation in order to maintain the tactile sensor normal to the phantom's surface. A depth camera and a computer vision algorithm are used to infer the phantom's shape and achieve DVD ADR shared control. Experimental results showcase how this leads to statistically significant performance improvement. Not only were the participants able to achieve more precise palpations, with up to 29.5% and 22.4% more accuracy in position and orientation, respectively, but the DVD ADR shared control allowed them to achieve a 8.8% better detection accuracy while needing 13.8% less time. The abovementioned results are all tested for statistical significance and achieved a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i>-value lower than 0.05.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".