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Record W4366982534 · doi:10.1002/advs.202207493

Amphibious Miniature Soft Jumping Robot with On‐Demand In‐Flight Maneuver

2023· article· en· W4366982534 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Science · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
FundersChinese University of Hong KongNational Natural Science Foundation of China
KeywordsControllabilityRobotJumpingKinematicsSimulationControl theory (sociology)Computer scienceControl engineeringEngineeringArtificial intelligencePhysicsControl (management)

Abstract

fetched live from OpenAlex

In nature, some semiaquatic arthropods evolve biomechanics for jumping on the water surface with the controlled burst of kinetic energy. Emulating these creatures, miniature jumping robots deployable on the water surface have been developed, but few of them achieve the controllability comparable to biological systems. The limited controllability and agility of miniature robots constrain their applications, especially in the biomedical field where dexterous and precise manipulation is required. Herein, an insect-scale magnetoelastic robot with improved controllability is designed. The robot can adaptively regulate its energy output to generate controllable jumping motion by tuning magnetic and elastic strain energy. Dynamic and kinematic models are developed to predict the jumping trajectories of the robot. On-demand actuation can thus be applied to precisely control the pose and motion of the robot during the flight phase. The robot is also capable of making adaptive amphibious locomotion and performing various tasks with integrated functional modules.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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