SnuggleBot the Companion: Exploring In-Home Robot Interaction Strategies to Support Coping With Loneliness
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it