Bibliographic record
Abstract
Abstract Due to inherent compliance and nonlinear behavior, modeling soft robots is highly complex. While the elasticity of their materials provides the robots with adaptability and resilience, it also causes undesirable effects. Cable-driven soft robots are particularly affected by frictional forces, which can significantly alter their deformed shape. Unfortunately, most existing cable friction models have been developed for rigid surfaces and do not account for the interactions between elastic cables and surfaces. As a result, research on cable-driven soft robots often neglects friction due to the difficulty of acquiring data and implementing mathematical models. This article introduces a novel friction model that considers the asperity behavior of both the cable and the friction surface. We propose a new methodology that accurately replicates friction interaction in soft robots, which requires as few as nine data points. The methodology is assessed on four distinct material interactions, comprising two cables with different diameters and materials, and two three-dimensional (3D)-printed surfaces made from polylactic acid (PLA) and thermoplastic polyurethane (TPU). By accurately estimating the nonuniform distribution of the joint deformation caused by friction, the new friction formulation achieves a 2.8% error in predicting a soft gripper’s tip locations, while the current state-of-the-art model shows a 16.1% error. We also demonstrate that, with an accurate friction model, it is possible to optimize the cable routing points to achieve the desired grasping strategy for a soft gripper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".