Variable Stiffness Soft Robotic Fingers Using Snap-Fit Kinematic Reconfiguration
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
Versatile and secure grasping in robotic systems remains a difficult challenge to address when objects possess a wide range of different properties (size, weight, friction coefficient, etc.). The human hand is often the primary source of inspiration for many technologies addressing this challenge, and a notable feature of our hands is that they can vary their stiffness to match the requirements of the task, e.g., become stiffer or more compliant depending on specific requirements. Many robotic devices have been proposed in the literature mirroring this capability, either using an adjustable internal tension mechanism similar to what happens with human tendons or another physical phenomenon yielding the same effect. This article proposes a new type of soft robotic fingers using a novel method to produce a variable stiffness achieved by modifying the kinematic structure of the fingers using snap-fit joints, a very simple alternative to most variable stiffness mechanisms. The resulting modification of the geometry and kinematics of the fingers, including their number of degrees of freedom, allows to greatly alter the intrinsic stiffness of the grasp produced by these fingers. A notable feature of the proposed new design is that one pair of fingers can be used to switch the stiffness of another pair if a dual arm robot is used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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