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Record W4410812506 · doi:10.1089/soro.2024.0157

Bioinspired Design for Energy-Efficient Soft Actuators Achieving Asymmetrical Spatiotemporal Deformation

2025· article· en· W4410812506 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

VenueSoft Robotics · 2025
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSoft roboticsActuatorBiomimeticsDeformation (meteorology)Computer scienceMechanical engineeringEngineeringControl engineeringMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This article presents a bioinspired pneumatic soft actuator designed to achieve asymmetrical spatiotemporal deformations, inspired by the dynamic motion of human walking. The actuator's key innovation is a half-crossing structure that enables controlled airflow to produce complex bending and linear motions using only two air tubes. This design significantly reduces structural complexity and energy consumption compared with conventional soft actuators, which often require multiple air channels to achieve similar deformations. The actuator mimics the stance and swing phases of locomotion, allowing precise multidirectional movements, including forward, backward, and turning motions. A passive feedforward control strategy further enhances movement flexibility without the need for complex feedback systems. Experimental results demonstrate the actuator's adaptability and efficiency when integrated into a hexapod robot, with optimized performance through adjustments in air pressure and cycle duration. This work offers a versatile and energy-efficient solution for adaptive locomotion in soft robotics, advancing the field through a novel approach to actuator design.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.877

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 head0.018
GPT teacher head0.241
Teacher spread0.222 · 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