Experimental Trials with a Shared Autonomy Controller Framework and the da Vinci Research Kit: Pattern Cutting Tasks using Thin Elastic Materials
Bibliographic record
Abstract
A technical challenge in robotic soft material cutting is to avoid large local deformations that result in inaccuracies or failure of the task. Additionally, reducing procedure time and human errors that occur due to fatigue and monotony are two of the most anticipated advantages of using robots in execution of repetitive subtasks for minimally invasive surgery. In our paper, we evaluate pattern cutting tasks of 2D elastic materials with shared control using the da Vinci Research Kit (dVRK). For this purpose, we developed a shared autonomy motion generator framework for pattern cutting. The framework registers user-defined Cartesian positions, creates smooth splines, interpolates the Cartesian positions, and generates a trajectory with Cartesian and joint constraints. While a pre-planned trajectory is being executed, the user may provide Cartesian offsets to modify the trajectory. We repeatedly cut shapes on 3 materials with different elasticity. Our shared control method achieved 100% success rate while performing a circular cutting task in a sheet of gauze. The user input compensated for deformations due to tearing. Task completion time of those experiments was 86 seconds (median) / 88 seconds (mean). Median and mean errors were 3.1 mm and 3 mm, respectively. Our work improves the success rate and time of completion of published pattern cutting tasks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".