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Record W2801392054 · doi:10.1139/tcsme-2003-0021

OBJECT SHAPE EXPLORATION AND RECOGNITION IN 2D USING A TWO-FINGERED ROBOTIC HAND

2004· article· en· W2801392054 on OpenAlexaffvenue
Yonghui Du, Gary M. Bone

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsObject (grammar)Computer visionRobotic handArtificial intelligenceComputer scienceSet (abstract data type)Position (finance)Degrees of freedom (physics and chemistry)Cognitive neuroscience of visual object recognitionTactile sensorRobotPhysics

Abstract

fetched live from OpenAlex

Exploration with a robotic hand is a method for measuring the shape and location of objects. This information may be used to recognize the object from a given set, or to generate a model of the object. A novel two-dimensional exploration method employing multiple wrapping grasps without tactile sensors is presented. An object shape model is produced from the finger position information. An efficient procedure for recognizing the object from a given reference set is presented. Experiments are conducted with a two-fingered robotic hand with five degrees-of-freedom. Objects with circular, square and triangular cross-sections are explored, modeled and recognized using as few as six grasps.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.228
Teacher spread0.187 · 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 teacher head, 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

Citations0
Published2004
Admission routes2
Has abstractyes

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