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Record W2152719922 · doi:10.1109/icsmc.1995.537824

Tactile sensing of point contact

2002· article· en· W2152719922 on OpenAlexaff
Ning Chen, Hong Zhang, R. Rink

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTactile sensorPoint (geometry)Moment (physics)AcousticsLayer (electronics)Computer visionComputationContact forceComputer scienceInverseArtificial intelligenceImage (mathematics)MathematicsMaterials sciencePhysicsGeometryAlgorithmClassical mechanicsRobot

Abstract

fetched live from OpenAlex

Contact location and force are among the most useful parameters for grasping and dextrous manipulation. This paper presents the theoretical results of extracting these parameters from a tactile sensor, which is an array of pressure (stress) transducers under an elastic layer. Using the model of a 3-D frictional point force acting on an elastic half-space, moment analysis is employed to interpret a tactile image. Analytical relationship between the first three moments of the tactile image and contact location and force (magnitude and direction) is established for the cases of a single-layer and two-layer one-dimensional tactile sensor and a single layer multi-dimensional tactile sensor. It is shown that the first three moments of a tactile image are sufficient to recover information about point contact. The corresponding inverse models have simple closed form for efficient computation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.812
Threshold uncertainty score0.997

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)0.0040.000

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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.

Study designBench or experimental
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

Citations8
Published2002
Admission routes1
Has abstractyes

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