Performance Augmentation of Underactuated Fingers' Grasps Using Multiple Drive Actuation
Bibliographic record
Abstract
In this paper, the performance augmentation of underactuated fingers through additional actuators is presented and discussed. Underactuated, also known as self-adaptive, fingers typically only rely on a single actuator for a given number of output degrees of freedom (DOF), generally equal to the number of phalanges. Therefore, once the finger is mechanically designed and built, little can be done using control algorithms to change the behavior of this finger, both during the closing motion and the grasp. We propose to use more than one actuator to drive underactuated fingers to improve the typical metrics used to measure their grasp performances (such as stiffness and stability). In order to quantify these improvements, two different scenarios are presented and discussed. The first one analyzes the impact of adding actuators along the transmission linkage of a classical architecture while the second focuses on a finger with a dual-drive actuation system for which both actuators are located inside the palm. A general kinetostatic analysis is first carried out and adapted to cover the case of underactuated fingers using more than one actuator. Typical performance indices are subsequently presented and optimizations are performed to compare the best designs achievable with respect to stiffness and grasp stability, depending on the number of actuators.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".