<i>Ocyropsis</i>‐Inspired Fast‐Swimming Transparent Soft Robots
Bibliographic record
Abstract
Abstract Marine creatures achieve effective survival in unstructured ocean environments via fast swimming or transparent camouflage. Aqueous soft robots, capable of reproducing soft features of marine creatures, have the advantages of safe biological interaction, high environmental adaptation, and noise‐free when compared with traditional rigid robots. Yet, there exists a persistent challenge to develop both fast and energy‐efficient aqueous soft robots that can achieve better underwater operation or exploration. Enlightened by the morphology and swimming strategy of Ocyropsis — a jellyfish‐like creature, Ocyropsis‐inspired robots (i.e., Ocyrobots) that merge electro‐hydraulic actuation and Ocyropsis‐type rowing mechanisms to achieve high‐performance underwater locomotion are developed. Ocyrobots demonstrate a record‐high speed of 1.1 body length/s, which is approximately three times of previously reported fastest jellyfish‐like robots while maintaining a low power consumption of 37 mW. Ocyrobots also exhibit an impressive turning speed of 34° s−1, enabling dexterous locomotion and effective obstacle avoidance in confined underwater scenarios. Attributed to the self‐developed highly reliable polymer‐based ionic gel, Ocyrobots possess remarkable advantages of full transparency and high durability, which improves their lifetime and reduces potential disturbances to underwater ecosystems. The unprecedented biomimetic idea in this study is essential in enlightening the prototyping of future aqueous soft robotics.
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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".