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Record W2295635403 · doi:10.5220/0005540000660073

RCON: Dynamic Mobile Interfaces for Command and Control of ROS-enabled Robots

2015· article· en· W2295635403 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
TopicModular Robots and Swarm Intelligence
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceMiddleware (distributed applications)Interface (matter)SoftwareUser interfaceRobotProcess (computing)Human–computer interactionEmbedded systemGraphical user interfaceMobile robotDistributed computingOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

The development of effective user interfaces for an autonomous system can be quite difficult, especially for devices that are to be operated in the field where access to standard computer platforms may be difficult or impossible. One approach in this type of environment is to utilize tablet or phone devices, which when coupled with an appropriate tool such as ROSBridge can be used to connect with standard robot middleware. This has proven to be a successful approach for devices with mature user interface requirements but may require significant software development for experimental systems. Here we describe RCON, a software tool that allows user interfaces on iOS devices to be configured on the device itself, in real time, in response to changes in the robot software infrastructure or the needs of the operator. The system is described in detail along with the accompanying communication framework and the process of building a user interface for a simple autonomous device.

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 score0.792
Threshold uncertainty score0.296

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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