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Record W3135248361 · doi:10.47839/ijc.6.2.443

HAPTIC HUMAN INTERFACES FOR ROBOTIC TELEMANIPULATION

2014· article· en· W3135248361 on OpenAlex
Emil M. Petriu, Pierre Payeur, Ana-Maria Creţu

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Computing · 2014
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsHaptic technologyStylusComputer scienceVirtual realityHuman–computer interactionArtificial intelligenceSet (abstract data type)Interface (matter)TeleroboticsComputer visionModalitiesTactile sensorRobotMobile robot

Abstract

fetched live from OpenAlex

Recent investigation in haptic man-robot interaction suggests that there are ultimately only two topical tactile feedback generation modalities for haptic human interfaces. These allow the human operator to handle either (i) temporary virtual reality-based material replicas of the local geometric and/or force profile at the contact areas of an unlimited set of generic objects that could virtually be handled during the manipulation, or (ii) permanent material replicas of a limited set of typical objects. In this paper, the two modalities are analyzed and examples of tactile human interfaces developed by the authors for telerobotic blind tactile exploration of objects, and for telerobotic hapto-visual stylus-style tool manipulation are presented to illustrate the proposed approaches. The necessary modelling of the elastic properties of 3D objects from experimental tactile and range imaging data is also presented using a neural network architecture that becomes an important component of the haptic interface.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.069
GPT teacher head0.358
Teacher spread0.289 · 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