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Record W2170935086 · doi:10.1109/robot.2007.363975

Performance Issues in Collaborative Haptic Training

2007· article· en· W2170935086 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

VenueProceedings - IEEE International Conference on Robotics and Automation/Proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsHaptic technologyTrainerComputer scienceHuman–computer interactionRobotVirtual realitySimulationTask (project management)Artificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This paper proposes a new multilateral position-position shared control architecture for dual-user haptic training. The proposed controller allows interaction between both users, the trainee and the trainer, as well as between the users and the virtual slave robot and environment. It also allows for the adjustment of the dominance of the trainer over the trainee in interaction with the virtual slave and environment through a dominance factor parameter. The issue of transparency in such collaborative haptic simulation system has been discussed. A performance index has also been defined to quantify the users' skill for a specific task under study. This metric is used to identify the maximum allowable dominance of the trainee over the trainer. Haptic simulation experiments have been carried out with two planar twin pantograph haptic devices and a simulated pantograph as the slave robot.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.281
Teacher spread0.244 · 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