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Record W4385413751 · doi:10.1109/lra.2023.3300253

Uncoupled Stability Dynamic Range for Hunt-Crossley Modeled Virtual Environments

2023· article· en· W4385413751 on OpenAlexaff
Seanna Oliver, Leonam Pecly, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsViscoelasticityHaptic technologyElasticity (physics)Stability (learning theory)Virtual machineSimulationVirtual realityComputer scienceViscosityRange (aeronautics)Parameter spaceStatistical physicsMechanicsControl theory (sociology)PhysicsMathematicsEngineeringThermodynamicsHuman–computer interactionArtificial intelligenceAerospace engineeringGeometry

Abstract

fetched live from OpenAlex

Viscoelastic medium is a common environment that is simulated as a virtual environment in haptic simulation systems. Kelvin-Voigt (KV) and Hunt-Crossley (HC) are models commonly used to simulate viscoelastic environments in haptic simulation systems. Due to the sample-and-hold process, the range of dynamics - viscosity and elasticity, that can be rendered in a stable way is limited. While uncoupled stability, as a stringent stability condition, has been analyzed for KV virtual environments, it has not been evaluated for HC environments. In this letter, we experimentally evaluate the range of dynamic parameters for each model that result in uncoupled stability. To compare the results, we map the HC parameters to the KV parameter space. To confirm the mapping, we conduct a user study to compare the viscosity and elasticity effects perceived by the users.

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: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.509

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 head0.016
GPT teacher head0.221
Teacher spread0.206 · 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
Published2023
Admission routes1
Has abstractyes

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