Exploring minimal nonverbal interruption in HRI
Bibliographic record
Abstract
Designing robotic behaviours capable of initiating an interruption will be extremely important as robots increasingly interact with people. Consequently, we explore the social impact of a minimal set of physical nonverbal cues that can be exhibited by a robot to initiate robot-human interruption: (a) speed of motion, (b) gaze, (c) head movement, d) rotation and (e) proximity to the person. We present two related studies evaluating this set. First, for requirements gathering, we observed the behaviour of interruption between humans, with a human actor attempting to interrupt other humans while being constrained to use only a set of behavioural cues that could be mimicked by a simple nonverbal robot. Next, we programmed a robot to exhibit similar social physical nonverbal cues, and tested their feasibility in a user study of robotic nonverbal interruption across interruption scenarios. Our results show that people were able to interpret interruption urgency from robot behaviour using only minimal nonverbal behavioural cues. These findings contribute to informing future designs of social human-robot interfaces.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".