Assisted design of linkage‐driven adaptive soft fingers
Bibliographic record
Abstract
Purpose Adaptive grippers are versatile end effectors that mechanically adapt their shapes to the objects they seize, allowing for soft and delicate grasps while still allowing for strong contact forces if needed and therefore they are well suited for industrial applications. The purpose of this paper is to present a software‐oriented approach to design optimal architectures of linkage‐driven adaptive (often a.k.a underactuated) fingers with three degrees of freedom. Design/methodology/approach The user of the software presented in this paper can design planar underactuated fingers following defined constraints. The software uses an algorithm able to compute the internal and contact forces generated, respectively, in and by the finger, it is also capable of automating the design of non‐straight links to eliminate mechanical interferences, and includes results from a topological synthesis to generate all possible architectures. The mechanisms are evaluated for many criteria such as the volume of their workspaces, stability, force isotropy, stiffness of their grasps, and compactness. Findings This article introduces 11 new designs of underactuated fingers for four different usages, and many of these variants are good candidates for a physical realization. One of the interesting results of this work is the recurrence of S3 variants coupled with torque amplifiers or closely resembling designs using many unrelated performance criteria. Originality/value This paper is the first, to the best of the authors' knowledge, to investigate the systematic design of underactuated fingers driven by linkages considering not one but dozens of mechanical architectures.
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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 | 0.000 |
| 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) | 0.001 | 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".