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Record W2036478283 · doi:10.1177/02783640122068227

Design of a Hollow Hexaform Torque Sensor for Robot Joints

2001· article· en· W2036478283 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

VenueThe International Journal of Robotics Research · 2001
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsMcGill UniversityCanadian Space Agency
Fundersnot available
KeywordsTorqueSensitivity (control systems)StiffnessDamping torqueRobotWork (physics)EngineeringFinite element methodComputer scienceControl theory (sociology)Control engineeringDirect torque controlMechanical engineeringArtificial intelligenceElectronic engineeringStructural engineeringPhysicsElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This work describes the design of a new one-axis torque sensor. It achieves the conflicting requirements of high stiffness for all six force and torque components, high sensitivity for the one driving torque of interest, and yet very low sensitivity for the other five force/torque components. These properties, combined with its donut shape and small size, make this sensor an ideal choice for direct-drive robotic applications. Experimental data validate the basic design ideas underlying the sensor’s geometry, the finite element model used in its optimization, and the advertised performance.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.194
GPT teacher head0.388
Teacher spread0.193 · 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