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Record W4317038508 · doi:10.1016/j.xcrp.2022.101241

Gecko-and-inchworm-inspired untethered soft robot for climbing on walls and ceilings

2023· article· en· W4317038508 on OpenAlexafffund
Jian Sun, Lukas Bauman, Yu Li, Boxin Zhao

Bibliographic record

VenueCell Reports Physical Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoft roboticsClimbingRobotBiomimeticsSoft materialsCreaturesGeckoClimbRoboticsExploitEngineeringArtificial intelligenceComputer scienceNanotechnologyMaterials scienceStructural engineeringAerospace engineeringEcologyBiology

Abstract

fetched live from OpenAlex

The climbing capabilities of living creatures such as geckos, tree frogs, and inchworms provide a promising platform for exploiting biomimetic soft climbing robots, but achieving this promise remains a grand challenge in materials science and engineering. Inspired by the adhesive characteristics of gecko toes and the gait of inchworms, here, we exploit the synergetic interactions of functional material components and design a hybrid biomimetic structure as a holistic soft robot, which can climb on walls and ceilings of different textures including glass, polyimide, and aluminum. In our design, the climbing behavior of the soft robot is based on dynamic attachment/detachment of a gecko adhesive pad and the periodic body deformation of the inchworm. The demonstrated synergetic combination of two biological principles and the assembly of individual components into one holistic soft robot provides scientific insights for utilizing biomimicry for soft robotics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations45
Published2023
Admission routes2
Has abstractyes

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