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Record W2138915230 · doi:10.1145/2658861.2658879

Ningyo of the CAVE

2014· article· en· W2138915230 on OpenAlexafffund
Nico Li, Stephen Cartwright, Ehud Sharlin, Mário Costa Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of Calgary
FundersCMG Reservoir Simulation Foundation
KeywordsCaveRobotHuman–computer interactionComputer scienceHuman–robot interactionKey (lock)Set (abstract data type)Reflection (computer programming)Quality (philosophy)Artificial intelligenceComputer securityGeographyArchaeology

Abstract

fetched live from OpenAlex

In this paper, we present a view of robots as physical agents submitting to a static infrastructure, allowing a computerized static system to use the robot as a dynamic puppet, which is a social agent that can communicate on physical and social terms with its human users and visitors. We demonstrate our approach with Ningy? of the CAVE, a prototype designed to allow a virtual reality CAVE facility to introduce its capabilities to human users and visitors. Through the robot, the CAVE is able to highlight capabilities and uses of the facility through performance, showmanship and physical actions to create an engaging interaction that conveys an overview of the facility and demonstrates its key functionalities. We examine the quality of the resulting engagement with preliminary reflection by several human visitors to our CAVE system. We believe that viewing robots as components of a greater and more capable computerized ecosystem is a less explored research path in social human-robot interaction, and hope that our Ningy? of the CAVE prototype could set the stage and inform some of the future research on this topic.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score0.992

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)0.0090.000

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 head0.024
GPT teacher head0.355
Teacher spread0.331 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2014
Admission routes2
Has abstractyes

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