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Record W2961410126 · doi:10.1002/aisy.201900019

Toward a Smart Compliant Robotic Gripper Equipped with 3D‐Designed Cellular Fingers

2019· article· en· W2961410126 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 Intelligent Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGrippersSoft roboticsStiffnessProcess (computing)3D printingBiomimeticsComputer scienceBendingMaterials scienceRobotMechanical engineeringEngineeringStructural engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Usual lightweight soft robotic bodies built with elastomer materials show lack of structural stiffness that limits their use in many practical applications. Herein, an architectured robotic body design with deformable cellular structures, which is easy to fabricate, lightweight, mechanically durable, and compliant while maintaining its resilience, is proposed. The cellular body design overcomes not only the stiffness limitation but also other drawbacks of most common soft bodies that may damage from high pressure or impact. An artificial cellular finger is printed together with embedded pressure sensors on the fingertip to form a functional system in a single‐building process with the advantage of multi‐material 3D printing. The integrated architectured grippers, composed of cellular fingers with a repeatable, reliable bending profile, demonstrate maximum gripping force as 16 N on actuation, with gripping capability of various objects. 3D cellular designs open up new possibilities for architectured robotic bodies that can immensely widen their space of 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.214
Teacher spread0.195 · 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