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Record W4411978044 · doi:10.1038/s44182-025-00035-2

RoboNautilus: a cephalopod-inspired soft robotic siphon for underwater propulsion

2025· article· en· W4411978044 on OpenAlexaff
Alexander E. White, Alexander Yin, Ang Li, Soohyeon Kang, Yuechao Wang, Leonardo P. Chamorro, Mihai Duduta

Bibliographic record

Venuenpj Robotics · 2025
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of Toronto
FundersConnecticut Space Grant College ConsortiumNational Aeronautics and Space Administration
KeywordsCephalopodUnderwaterMarine engineeringPropulsionSoft roboticsFisheryoctopus (software)Computer scienceEngineeringGeologyArtificial intelligenceBiologyOceanographyRobotAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Early nautiloids evolved siphon-like structures hundreds of millions of years ago as a propulsion mechanism for maneuvering in underwater environments. Over time, siphons became the cephalopod method for jetting locomotion, but few bio-mimetic soft robotic replicas have been developed. The principal challenge is the limited selection of solid-state, active soft materials that can replicate the function of the active mantle in a natural siphon. Here, we present a Nautilus-inspired propulsion system that employs multilayered solid-state dielectric elastomer actuators (DEAs) to produce an artificial siphon. The system features a soft robotic siphon, onboard sensors for semi-autnonomous operation, and a 3D-printed shell with an internal air pocket for buoyancy and self-righting ability. Through analytical modeling and empirical approaches, we develop a soft muscle for vortex ring formation and thrust output of 17 mN at 2 kV. These findings provide a framework for designing soft actuators that can be used as new propulsors to enable efficient (Cost of Transport = 2.51), low-noise, underwater locomotion for exploration and environmental monitoring applications.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.242
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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