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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.045
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.003

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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