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Record W2138697946 · doi:10.1109/3477.979960

Hybrid resistive tactile sensing

2002· article· en· W2138697946 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

VenueIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2002
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsResistive touchscreenTactile sensorSTRIPSComputer scienceProximity sensorSurface (topology)GridElectronic engineeringFlexibility (engineering)Dimension (graph theory)Set (abstract data type)RobotAcousticsEngineeringElectrical engineeringArtificial intelligenceComputer visionPhysicsMathematics

Abstract

fetched live from OpenAlex

The paper describes a novel design of a robot tactile sensor, called hybrid tactile sensor, based on analog resistive technology. The design employs a set of parallel analog resistive sensing strips, rather than a two-dimensional (2D) grid of sensing elements, to sample the sensor surface. It is therefore analog in one dimension and discrete in the other. As a result, the number of samples that need to be obtained is significantly reduced, as is the number of connectors in the sensor circuitry. In addition, the sensor design scales easily to a large sensing surface area, and offers flexibility in sensor geometry. However, only the contact location and shape can be sensed, but not the contact force. The paper provides the theoretical model of the sensor, and presents simulation and experimental results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.209
Teacher spread0.185 · 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