Experimental evaluation of a small-sized continuum robot for grasping tasks
Bibliographic record
Abstract
This study explores catheters, small-sized continuum robots (CRs), in medical procedures, utilizing a recently developed comprehensive classification system known as the “CR-based taxonomy”. This work specializes the proposed taxonomy for small-sized CRs with medical applications through semi-structured interviews with researchers in this field. Subsequently, it provides a comprehensive analysis of grasp taxonomy, encompassing aspects such as grasp stability, grasp adaptability, and the inherent characteristics of manipulated objects and tasks. This analysis is conducted within the context of the “CR-based taxonomy” to assess its feasibility in a practical scenario involving a small-sized CR. Based on this analysis, fixed-tip grasp, hook grasp, and power form closure grasp (PFG) with a grasp stability of 18 have the highest stability, while platform/pull/push grasp and expandable grasp have the lowest stability. Moreover, PFG and precision multi-contact grasp have superior adaptability compared to other grasp families. Also, the planar payload of selected grasp families is much higher than the spatial payload of those families; however, the grasp size remains equal for both dimensions. This endeavor lays the groundwork for more in-depth investigations into catheter applications and offers valuable guidance to medical device developers in the creation of enhanced medical instruments.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".