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Record W2059150255 · doi:10.1109/tmech.2011.2141673

Dual-User Teleoperation Systems: New Multilateral Shared Control Architecture and Kinesthetic Performance Measures

2011· article· en· W2059150255 on OpenAlexaff
Behzad Khademian, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2011
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsTeleoperationKinesthetic learningHaptic technologyComputer scienceDominance (genetics)Dual (grammatical number)Controller (irrigation)RobotArchitectureSimulationHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a novel six-channel multilateral shared control architecture for dual-user teleoperation systems. The proposed controller allows interaction between two users as well as the slave and environment through a dominance factor. The dominance factor adjusts the authority of the users over the slave robot and environment. The proposed controller is implemented on a haptic simulation test bed consisting of two planar twin pantograph haptic devices and a simulated pantograph as the slave robot. To analyze the kinesthetic performance of the proposed multilateral shared controller, a number of performance measures are extended or proposed. These measures are evaluated analytically and experimentally for various types of environments, users’ grasps, and levels of dominance of the users over the task.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.185
Teacher spread0.168 · 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

Citations103
Published2011
Admission routes1
Has abstractyes

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