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Record W4400142656 · doi:10.1145/3643834.3660702

SnuggleBot the Companion: Exploring In-Home Robot Interaction Strategies to Support Coping With Loneliness

2024· article· en· W4400142656 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

VenueDesigning Interactive Systems Conference · 2024
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLonelinessCoping (psychology)RobotPsychologyComputer scienceHuman–computer interactionHuman–robot interactionApplied psychologyInternet privacySocial psychologyPsychotherapistArtificial intelligence

Abstract

fetched live from OpenAlex

We explored the use of three robot interaction strategies to support people living with loneliness (physical comfort, social engagement, requiring care), by building these into a robot prototype and deploying the robots into homes for long-term evaluation. We placed our original prototype, SnuggleBot, unsupervised into the homes of seven people for at least 7 weeks (optionally up to 6 months), with bi-weekly interviews, to investigate how people engage with our three robot interaction strategies. Our qualitative analysis illuminated how people engaged the robot based on all three interaction strategies. Further, some participants showed signs of bonding with the robot as well as self-reported wellbeing benefits, while some participants failed to achieve sustained use over time. Our results provide strong support for future research into robots developed with our interaction strategies, and general potential for supporting wellbeing.

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), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.202
GPT teacher head0.397
Teacher spread0.196 · 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