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Record W2055256495 · doi:10.1115/1.2159029

Geometric Design of Three-Phalanx Underactuated Fingers

2005· article· en· W2055256495 on OpenAlexafffund
Lionel Birglen, Clément Gosselin

Bibliographic record

VenueJournal of Mechanical Design · 2005
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité LavalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsUnderactuationGRASPPulleyActuatorMechanism (biology)PhalanxObject (grammar)Computer scienceDegrees of freedom (physics and chemistry)Position (finance)Control theory (sociology)EngineeringArtificial intelligenceStructural engineeringPhysicsRobotControl (management)AnatomyBiology

Abstract

fetched live from OpenAlex

This paper studies the grasp stability of two classes of three-phalanx underactuated fingers with transmission mechanisms based on either linkages or tendons and pulleys. The concept of underactuation in robotic fingers—with fewer actuators than degrees of freedom—allows the hand to adjust itself to an irregularly shaped object without complex control strategy and sensors. With a n-phalanx finger, n contacts (one for each phalanx) are normally required to statically constrain the finger. However, some contact forces may be lacking due to either the transmission mechanism, or simply the object size and position. Thus, one may define an ith order equilibrium, when the finger is in static equilibrium with i missing contacts. In this paper, the case for which n=3 is studied with a particular emphasis on the cases for which i=1 and i=2. The fact that some contact forces do not appear or are negative, can lead in some cases to the ejection of the object from the hand, when no equilibrium configuration is achieved.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.073
GPT teacher head0.256
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations96
Published2005
Admission routes2
Has abstractyes

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