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Record W2092434811 · doi:10.1109/iros.2013.6697005

Energy-consistent haptic rendering of contact forces

2013· article· en· W2092434811 on OpenAlexafffund
Arash Mohtat, József Kövecses

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyHope Funds for Cancer Research
KeywordsRendering (computer graphics)Computer scienceHaptic technologyContact forceFidelityStiffnessDissipationSimulationComputer graphics (images)EngineeringPhysicsStructural engineeringClassical mechanics

Abstract

fetched live from OpenAlex

Enhancing the realism of the perceived contact force is a primary challenge in haptic rendering of virtual walls (VWs) and objects (VOs). For VOs, this goal directly translates into accurate rendering of not only stiffness, but also mass. The most challenging situation arises when the stiffness of the object is large, its mass is small, and sampling is slow. To address this challenge, a framework entitled high-fidelity haptic rendering (HFCR) has been developed. The HFCR framework is composed of the following three main strategies: an energy-consistent rendering of the contact force, smooth transition between contact modes, and remaining leak dissipation. The essence of all these strategies is to make the energy of the VO emulate its continuous-time counterpart. This is achieved through physically meaningful modifications in the constitutive relations to suppress artificial energy leaks. This paper reports simulation and experiments involving the one-dimensional canonical model of a VO to illustrate the HFCR framework and compare it to the existing methods. Results demonstrate the promising stability and force rendering fidelity of this framework.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.899

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)0.0010.000

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 head0.012
GPT teacher head0.183
Teacher spread0.172 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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