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Record W1988178041 · doi:10.1109/icinfa.2014.6932785

From ROS to unity: Leveraging robot and virtual environment middleware for immersive teleoperation

2014· article· en· W1988178041 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
TopicRobotics and Automated Systems
Canadian institutionsYork University
Fundersnot available
KeywordsTeleoperationComputer scienceMiddleware (distributed applications)Human–computer interactionSoftwareVirtual realityRobotVirtual machineSoftware frameworkProtocol (science)Task (project management)Interface (matter)Software developmentComponent-based software engineeringSoftware engineeringDistributed computingOperating systemSystems engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Virtual reality systems are often proposed as an appropriate technology for the development of teleoperational interfaces for autonomous and semi-autonomous systems. In the past such systems have typically been developed as “one off” experimental systems in part due to a lack of common software systems for both robot software development and virtual environment infrastructure. More recently, common frameworks have begun to emerge for both robot control (e.g., ROS) and virtual environment display and interaction (e.g., Unity). Here we consider the task of developing systems that integrate these two environments. A yaml-based communications protocol over web sockets is used to glue the two software environments together. This allows each system to be controlled using standard software toolkits independently while providing a flexible interface between these two infrastructures.

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 score0.688
Threshold uncertainty score0.363

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 head0.011
GPT teacher head0.185
Teacher spread0.175 · 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

Citations58
Published2014
Admission routes1
Has abstractyes

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