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Record W2112858669 · doi:10.1109/icma.2006.257628

An Architecture for Robotic Hardware-in-the-Loop Simulation

2006· article· en· W2112858669 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
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsModular designHardware-in-the-loop simulationArchitectureComputer scienceEmbedded systemComputer architectureHardware architectureRobotLoop (graph theory)Control engineeringEngineeringOperating systemArtificial intelligenceSoftware

Abstract

fetched live from OpenAlex

Hardware-in-the-loop simulation is an increasingly popular engineering tool for its effectiveness in maintaining a balance between the two competing demands of: a) well designed and thoroughly tested systems, and b) reduced development time and costs to remain competitive. This paper proposes an architecture for applying hardware-in-the-loop simulation techniques to the design and testing of robot manipulators. Potential benefits of this architecture include allowing concurrent development of hardware and control system components and providing a reusable platform for reconfigurable manipulators through a generic and modular structure. An implementation of this architecture in the preliminary phases of development is presented here to demonstrate how these benefits can be realized

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 categoriesnone
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 score0.958
Threshold uncertainty score0.256

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)0.0000.000

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations40
Published2006
Admission routes1
Has abstractyes

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