Improvising with an Audience-Controlled Robot Performer
Bibliographic record
Abstract
In improvisational theatre (improv), actors perform unscripted scenes together, collectively creating a narrative. Audience suggestions introduce randomness and build audience engagement, but can be challenging to mediate at scale. We present Robot Improv Puppet Theatre (RIPT), which includes a performance robot (Pokey) who performs gestures and dialogue in short-form improv scenes based on audience input from a mobile interface. We evaluated RIPT in several initial informal performances, and in a rehearsal with seven professional improvisers. The improvisers noted how audience prompts can have a big impact on the scene - highlighting the delicate balance between ambiguity and constraints in improv. The open structure of RIPT performances allows for multiple interpretations of how to perform with Pokey, including one-on-one conversations or multi-performer scenes. While Pokey lacks key qualities of a good improviser, improvisers found his serendipitous dialogue and gestures particularly rewarding.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".