Bibliographic record
Abstract
This paper presents the design and optimization of a self-adaptive, a.k.a.underactuated, finger targeted to be used with collaborative robots.Typical robots, whether collaborative or not, mostly rely on standard translational grippers for pickand-place operations.These grippers are constituted from an actuated motion platform on which a set of jaws is rigidly attached.These jaws are often designed to secure a precise and limited range of objects through the application of pinching forces.In this paper, the design of a self-adaptive robotic finger is presented which can be attached to these typical translational gripper to replace the common monolithic jaws and provide the gripper with shape-adaptation capabilities without any control or sensors.A new design is introduced here and specially optimized for collaborative robots.The kinetostatic analysis of this new design is briefly discussed and then followed by the optimization of relevant geometric parameters.Finally, a practical prototype attached to a very common collaborative robot is demonstrated.While the resulting finger design could be attached to any translational gripper, specifically targeting collaborative robots as an application allows for more liberty in the choice of design parameters as will be shown and the optimized parameters that are found take advantage of this property.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".