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Record W2151545824 · doi:10.1109/imtc.2008.4547261

Modelling Contact Forces for 3D Interactive Peg-in-Hole Virtual Reality Operations

2008· article· en· W2151545824 on OpenAlexaff
Jilin Zhou, Nicolas D. Georganas, Emil M. Petriu, Xiaojun Shen, Francois Marlic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHaptic technologyVirtual realityCollision detectionComputer scienceTorqueRendering (computer graphics)Contact forceHigh fidelityFidelitySimulationImmersion (mathematics)Virtual machineHuman–computer interactionMixed realityCollisionComputer graphics (images)EngineeringPhysics

Abstract

fetched live from OpenAlex

Haptic interfaces allow virtual reality users to experience the feeling of interacting forces while manipulating virtual objects. This paper discusses a model which is able to render in real-time the contact force/torque occurring in peg-in-hole operations in an interactive virtual reality environment. Results show that our implementation of haptic rendering can be well maintained at the required 1 KHz for a necessary fidelity without separating it from the time consuming collision detection. It is noticed that the hand's grasping posture on the tool should be an explicit parameter in the force/torque output function since it affects the feedback perception.

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 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 categoriesnone
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 score0.833
Threshold uncertainty score0.346

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)0.0000.000

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 head0.042
GPT teacher head0.266
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2008
Admission routes1
Has abstractyes

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