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Record W4312416017 · doi:10.1109/hri53351.2022.9889346

T-Top, a SAR Experimental Platform

2022· article· en· W4312416017 on OpenAlex
Marc-Antoine Maheux, Charles Caya, Dominic Létourneau, François Michaud

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · 2022
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsInstitut interdisciplinaire d'innovation technologique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModalitiesComputer scienceHuman–computer interactionPerceptionRobotStrengths and weaknessesDementiaSoftwareArchitectureHealth careMultimediaArtificial intelligencePsychologyMedicine

Abstract

fetched live from OpenAlex

During these past years, Socially Assistive Robots (SARs) have been used to study the benefits of their uses with elderly people and people with dementia for healthcare purposes. Yet, almost all SARs have somewhat limited perception capabilities or respond using simple pre-programmed behaviors and reactions, providing limited or repetitive interaction modalities. To overcome these limitations and take into consideration the strengths and weaknesses of SARs in healthcare settings, this paper presents T-Top, a tabletop robot designed with advanced audio and vision sensors, deep learning perceptual processing and telecommunication capabilities. Designed as a open hard-ware/software platform, the objective with T-Top is to provide an experimental platform that can implement richer interaction modalities and develop higher cognitive abilities from interacting with people.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.2790.003

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 head0.338
GPT teacher head0.501
Teacher spread0.163 · 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