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Record W2138444350 · doi:10.1109/vl.1998.706166

Visual programming for robot control

2002· article· en· W2138444350 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
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceProgrammerReactive programmingInductive programmingFunctional reactive programmingProgramming languageVisual programming languageProgramming domainEvent-driven programmingProcedural programmingProgramming paradigmDomain (mathematical analysis)Artificial intelligenceRobotRepresentation (politics)Process (computing)Symbolic programmingHuman–computer interaction

Abstract

fetched live from OpenAlex

The primary advantage of visual programming languages is that they directly represent the structure of algorithms and data, thereby enhancing the programmer's ability to build and comprehend programs. If the programming domain consists of physical objects with observable behaviour, such as a robot and its environment, then incorporating the obvious visual representations of these objects directly into the programming process may further increase the programmer's effectiveness and accuracy. We propose a robot programming system consisting of two parts; a definition module with which to describe the structure, function and visual representation of a specific robot, and a programming module that uses this description to enable programming by direct manipulation. We describe the visual editors that constitute the first of these modules, discuss the underlying structure generated by it, and briefly show how this structure can be used in the second module.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.988
Threshold uncertainty score0.320

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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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

Citations22
Published2002
Admission routes1
Has abstractyes

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