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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 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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 source (direct Gemma or distilled Codex), 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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