MétaCan
Menu
Back to cohort
Record W2138697946 · doi:10.1109/3477.979960

Hybrid resistive tactile sensing

2002· article· en· W2138697946 on OpenAlexaff
Hong Zhang, Ernest So

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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

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 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

Citations63
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)Same topicRobot Manipulation and LearningFrench-language works237,207