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Record W1838525719 · doi:10.1109/robot.1995.525577

Mechanisms for haptic feedback

2002· article· en· W1838525719 on OpenAlexaffabout
R. Hui, Alain Ouellet, A. Wang, Paul G. Kry, Stefan B. Williams, George Vukovich, Walter Peruzzini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsHaptic technologyComputer scienceHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

This article describes work in progress at the Canadian Space Agency on the design and implementation of haptic devices. Haptic devices are a special class of robotic mechanisms for which structural transparency is a foremost design criterion. Also notable is the fact that often, three or four degrees of freedom, rather than six as in general robotic tasks, are sufficient for many haptic applications. Furthermore, in order to make these devices readily available to many users, it is necessary their kinematic models be sufficiently simple such that they can be controlled by inexpensive means. Various three and four-DOF mechanisms, some of which recently developed by the Canadian Space Agency, are discussed herein in terms of their suitability for haptic applications. For five or six-DOF applications, the concept of the virtual handle is introduced, reducing the problems and complexity usually associated with mechanisms with a high number of degrees of freedom.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0290.005

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 head0.023
GPT teacher head0.194
Teacher spread0.170 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations15
Published2002
Admission routes2
Has abstractyes

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