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Record W2143092842 · doi:10.1109/iros.2008.4650575

A New Method for Online Parameter Estimation of Hunt-Crossley Environment Dynamic Models

2008· article· en· W2143092842 on OpenAlex

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsEstimation theoryInitializationConvergence (economics)Computer scienceSensitivity (control systems)Nonlinear systemSet (abstract data type)Identification (biology)System identificationApplied mathematicsAlgorithmControl theory (sociology)Mathematical optimizationMathematicsArtificial intelligenceData modelingControl (management)EngineeringPhysics

Abstract

fetched live from OpenAlex

Online estimates of unknown environment dynamics are used for the control of robotic contact tasks. The Hunt-Crossley nonlinear dynamic model of environments has been shown to be more consistent with the physics of contact, compared to the classical linear models, such as Kelvin-Voigt. This paper proposes a new method for online parameter estimation of Hunt-Crossley model and provides a mild set of conditions for guaranteed unbiased estimation. The rate and the sensitivity of convergence to parameter initialization and system parameter changes are numerically evaluated and compared for both the proposed method and an existing 2-stage identification method.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.406
Threshold uncertainty score0.347

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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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

Quick stats

Citations49
Published2008
Admission routes1
Has abstractyes

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