Investigating frictional contact behavior for soft material robot simulations
Bibliographic record
Abstract
Abstract The ability to interact safely with the environment is known as one of the major advantages of soft robots (SRs). Due to their low material stiffness, these continuously deformable robots offer inherent flexibility. These advantages make them suitable for application that involve human-robot collaboration in industrial settings as well as medical application such as minimally invasive surgery. To date only few research groups have analyzed the contact and frictional behavior of soft robots. In fact, the contact behavior is often oversimplified or neglected. Motivated by the idea to bridge this gap, this work presents measurements and the resulting coefficient of friction (COF) for silicone rubbers that are widely used in the field of SRs and different contact partners which depend on contact pressure and ambient temperature. From these measurements, a more representative contact model is established and used to more accurately simulate soft material robots’ frictional contact behavior. Moreover the influence of friction and therefore the need to implement frictional behavior is demonstrated for a typical application of a SR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".