The kinematic preshaping of triggered self-adaptive linkage-driven robotic fingers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. In this paper, the issue of the kinematic – as opposed to dynamic – preshaping of self-adaptive robotic fingers driven by linkages is discussed. A method to obtain designs of these fingers capable of various behaviours during their closing motions is presented. The method is based on using triggered passive elements in carefully selected joints of the finger and the selection or optimization of geometric parameters to obtain particular kinematic relationships between the motions of the phalanges. This method is very general and can be applied to any self-adaptive robotic finger in order to obtain many different types of closing motions. Examples given in this paper are focusing on two different preshaping motions, the first one aims at allowing pinch grasps while the second mimics a human finger. The fundamental aim of this paper is to show that various preshapings of self-adaptive fingers are possible, not just one, and to give two step-by-step examples. This paper was presented at the IFToMM/ASME International Workshop on Underactuated Grasping (UG2010), 19 August 2010, Montréal, Canada.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it