Emergent Sequential Motion Through Compliant Auxetic Shells
Bibliographic record
Abstract
Though they are compliant, nimble and morpho-logically intelligent, fluidic soft robots often rely on bulky components for power and actuation. This work contributes a design methodology which enables development of soft fluidic robots that move in a sequenced fashion, enabling lightweight devices with embodied intelligence. Bezier-curved beams were introduced as a design building block whose antagonistic placement results in Representative Auxetic Element (RAE) that can be patterned on inflatable shells. Kinematics and loading behaviour of these design building blocks were studied through Finite Element Analysis (FEA). We give a methodology for patterning RAEs on cylindrical and conic shells to create soft fluidic components that move (motion components) and those that delay fluid flow (pinch components). We verify the physical concepts governing the design methodology through two prototype devices that produce sequenced motion under a single fluidic input. Devices using this framework have the potential to perform complicated sequenced motions with lightweight control components.
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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) | 0.001 | 0.002 |
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".