Investigating the effect of morphology on the terrestrial gaits of amphibious fish using a reconfigurable robot
Bibliographic record
Abstract
Abstract The relationship between morphology and locomotion performance in amphibious fish remains poorly understood, particularly in axial-appendage-based and appendage-based movements. To address this, we introduce Polymander, a reconfigurable robot capable of mimicking Polypterus -like walking and mudskipper-like crutching, enabling systematic investigation of body length and limb movement. Using a CPG-driven controller, we optimize locomotion patterns via multi-objective optimization in simulation, comparing resulting Pareto fronts across different morphological configurations. Our results reveal that (1) mudskipper-like crutching is better suited for short bodies, while Polypterus -like walking is better suited for longer bodies; (2) symmetric anterior-to-posterior motion of the limbs is optimal for crutching, while increased anterior limb movement benefits Polypterus -like walking; and (3) sufficient limb strength is necessary for crutching but less so for walking, where axial bending mitigate its effects. Overall, our findings provide a potential explanation of why Polypterus and mudskippers adopt their distinct gaits, emerging as optimal solutions for their morphology within the broader space of all possible gaits.
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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 | 0.000 |
| 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 | 0.000 |
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".