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Record W4391128804 · doi:10.1109/toh.2024.3357070

Haptic Interactions Subject to Variable Latency

2024· article· en· W4391128804 on OpenAlexafffund
Chantal Hutchison, Joseph Hewlett, Siamak Arbatani, Antoine Weill–Duflos, József Kövecses

Bibliographic record

VenueIEEE Transactions on Haptics · 2024
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsCM Labs Simulations (Canada)McGill University
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsTeleoperationHaptic technologyLatency (audio)Computer scienceVirtual realityVirtual machineTeleroboticsSimulationHuman–computer interactionVariable (mathematics)Artificial intelligenceRobotMobile robotOperating system

Abstract

fetched live from OpenAlex

Model-Mediated Teleoperation (MMT) between a haptic device and a remote or virtual environment uses a local model of the environment to compensate for latency of communication. MMT is often case-specific, and requires underlying latency distributions to be known. We propose a novel approach - which we refer to as the DelayRIM - which uses the time-stepping aspect of a Reduced Interface Model for the environment to render an up-to-date force to the haptic device from the delayed information. RIM is applicable to any physical or virtual system, and the DelayRIM itself makes no underlying assumption about the latency distribution. We show that for realistic variable delays, the DelayRIM improves transparency compared to other methods for a virtual drone bilateral teleoperation scenario.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes2
Has abstractyes

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