Bibliographic record
Abstract
Social robots must be believable to be effective; but creating believable, affectively expressive robot behaviours requires time and skill. Inspired by the directness with which performers use their voices to craft characters, we introduce Voodle (vocal doodling), which uses the form of utterances -- e.g., tone and rhythm -- to puppet and eventually control robot motion. Voodle offers an improvisational platform capable of conveying hard-to-express ideas like emotion. We created a working Voodle system by collecting a set of vocal features and associated robot motions, then incorporating them into a prototype for sketching robot behaviour. We explored and refined Voodle's expressive capacity by engaging expert performers in an iterative design process. We found that users develop a personal language with Voodle; that a vocalization's meaning changed with narrative context; and that voodling imparts a sense of life to the robot, inviting designers to suspend disbelief and engage in a playful, conversational style of design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.024 |
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".