Enhancing emotional support in human-robot interaction: Implementing emotion regulation mechanisms in a personal drone
Bibliographic record
Abstract
We propose that social robots can enhance their social abilities by supporting peoples' emotional needs. We examined this concept by implementing four different mechanisms aimed at providing Emotional Support in a personal drone. These mechanisms (Affective Empathy, Cognitive Empathy, Positive Emotion Regulation (PER), and a Reasoning mechanism (yoU-turn)) provide various aspects of support ranging on the Emotional-Reasoning spectrum. In an online study ( N = 95), first, participants were asked to sequentially recall situations where they experienced one of six emotional states (i.e., being calm, bored, excited, hyperactivated, scared, or sleepy). Following each induced emotion, participants ranked their preferred drone response to their specific emotional state. Results indicate that participants' preferences were based on the valence of their emotional state, emphasizing the need for social drones to have multiple response mechanisms to support their users. This work contributes to the field of human-robot interaction by implementing validated support mechanisms into a robotic system as its emotional responses. • A social drone providing appropriate responses can support users' emotional needs. • Social response mechanisms were implemented into a drone to form an emotional response. • The drone provides responses that match users' emotion-based preferences. • Affective Empathy was the most preferred response for positive valence emotions. • Positive Emotion Regulation was the preferred response for negative valence emotions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".