Bibliographic record
Abstract
Abstract Due to inherent compliance and non-linear behaviour, modelling 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 the mathematical models. This paper introduces a novel friction model that considers the asperity behaviour 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 3D-printed surfaces made from PLA and 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.
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.
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 | 0.000 | 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 | 0.000 |
| 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 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".