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Record W2109824152 · doi:10.1109/roman.1995.531930

Telerobotic control using augmented reality

2002· article· en· W2109824152 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
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceRemote controlTeleroboticsAugmented realityVirtual realityControl (management)Human–computer interactionTeleoperationRobotArtificial intelligenceMobile robotOperating system

Abstract

fetched live from OpenAlex

A taxonomy for classifying human mediated control of remote manipulation systems is proposed, based on three dimensions: degree of machine autonomy, level of structure of the remote environment, and extent of knowledge, or modellability, of the remote world. For certain unstructured and thus difficult to model environments, a case is made for remote manipulation by means of director/agent control, rather than telepresence. The ARGOS augmented reality toolkit is presented, as a means for gathering quantitative spatial information about-i.e., for interactively creating a partial model of a remotely viewed 3D worksite. This information is used for off-line local programming of the remote manipulator-i.e. virtual telerobotic control-and when ready the final commands are transmitted for execution to the manipulator control system.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.999

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)0.0020.000

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 head0.054
GPT teacher head0.224
Teacher spread0.169 · 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

Citations99
Published2002
Admission routes1
Has abstractyes

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