Proxy Test Structures for Improved Hyperelastic Material Parameter Estimation for Soft Robotic Components
Bibliographic record
Abstract
Soft robotics relies on compliant materials like silicones that enable gentle and adaptive interaction with the environment. For effective sim-to-real transfer, accurate mechanical simulations are essential. Soft materials are typically modelled using hyperelastic formulations such as Ogden. Material parameters are obtained from standardized test structures via least squares fitting, a process that can be ill-posed. Here, we extend the standard approach by introducing design-specific 2D axisymmetric proxy test structures (2DAxTS) that better capture the behaviour of complex geometries, such as artificial silicone fingertips. Global optimization on these proxies improved agreement between experimental and simulated force-displacement (FD) curves, reducing the normalized root mean squared error by 39.1% and 17.8% for two different designs. This highlights the importance of geometry-aware material parameterization for enhancing simulation fidelity in soft robotics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".