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 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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".