MétaCan
Menu
Back to cohort
Record W2164834344 · doi:10.1109/robot.1996.509246

Curvature based shape estimation using tactile sensing

2002· article· en· W2164834344 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCurvatureTactile sensorSurface (topology)Computer visionComputer scienceSpline (mechanical)Point (geometry)Artificial intelligenceMathematicsGeometryEngineeringRobotMechanical engineering

Abstract

fetched live from OpenAlex

Proposes an approach-"Blind Man's" approach-to shape description in which tactile information is sensed from the fingertips of a dexterous hand. Using this contact information, we investigate two complementary methods for curvature estimation. The first method is based on rolling one finger to estimate curvature at a point on the surface. We use Montana's equations for estimating curvature at a point using simulations and analyze the sensitivity of the approach to noise. The second method uses multiple fingers to slide along a surface while sensing contact points and surface normals. We present a method to extract the shape properties of a patch obtained by fitting a B-spline surface to this multi-fingered sweep across the surface of the object. The method enables us to extract higher level shape information based on the curvature properties of patch.

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 categoriesInsufficient payload (model declined to judge)
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 score0.874
Threshold uncertainty score0.999

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)0.0020.000

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 head0.042
GPT teacher head0.232
Teacher spread0.190 · 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

Quick stats

Citations28
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicRobot Manipulation and LearningFrench-language works237,207