Configuration identification of on-demand variable stiffness strain-limiting layers in zig-zag soft pneumatic actuators using deep learning methods
Bibliographic record
Abstract
Soft pneumatic actuators (SPAs) typically offer a fixed trajectory, resulting in one specific tip motion for a given range of pressure. When multiple trajectories are needed, these actuators require re-fabrication with altered structural designs, with different lengths, chamber sizes, and wall thicknesses etc. Passive modular variable stiffness SPAs present a significant advantage by enabling the realization of many distinct trajectories without structural redesign. Although various mathematical modeling techniques are widely used to predict their tip motion by treating it a kinematics problem, solving the inverse problem in the presence of modular strain-limiting layer (SLL) configurations is challenging. It is essential to determine the configuration of such a slender actuator in the form of a robot manipulator to deploy it for a specific function and application without re-fabricating them, by simply varying the SLL per the configuration required for a particular tip-point trajectory. To this aim, this paper introduces a hybrid methodology (based on feed-forward neural network) and a convolutional neural network-based method to predict the required SLL configuration for a particular tip trajectory of the SPA. This methodology is generic enough to apply to such actuators to predict their configuration as per their specific tip point trajectory in Cartesian space. The results presented for a slender SPA have demonstrated that the proposed method has predicted its configurations for a range of applications typified by an endoscope prototype, a soft robotic gripping application, and a system mimicking human finger movement with an average error of 1.65%. This study offers a versatile methodology for "function and application specific" SPAs or robot manipulators without re-fabricating them, by strategically combining SLL and machine learning-based prediction to generate a specific trajectory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".