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Record W2101644100 · doi:10.1109/tsmcb.2009.2038357

Comparative Analysis of 3-D Robot Teleoperation Interfaces With Novice Users

2010· article· en· W2101644100 on OpenAlexafffund
Daniel Labonté, Patrick Boissy, François Michaud

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2010
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsTeleoperationModalitiesComputer scienceHuman–computer interactionVisualizationRobotMetric (unit)Interface (matter)TeleroboticsUser interfaceMultimediaArtificial intelligenceMobile robotEngineering

Abstract

fetched live from OpenAlex

Being able to act remotely in our homes could be very useful in providing various services such as surveillance and remote interventions, which are key features for telehomecare applications. In addition to navigation and environmental challenges that a telepresence robot would face in home settings, the system requires an appropriate teleoperation interface for safe and efficient usage by novice users. This paper describes the design criteria and characterizes visualization and control modalities of user interfaces with a real robot. By considering the user's needs along with the current state of the art in teleoperation interfaces, two novel mixed-reality visualization modalities are compared with standard video-centric and map-centric perspectives. We report teleoperation trials under six different task scenarios with a sample of 37 novice operators in homelike conditions. The results based on three quantitative metrics and one qualitative metric outline under which conditions the novel mixed-reality visualization modalities significantly improve the performance of novice 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 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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.234
Teacher spread0.218 · 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 designObservational
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

Citations76
Published2010
Admission routes2
Has abstractyes

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