Expectations Vs. Reality: Unreliability and Transparency in a Treasure Hunt Game With Icub
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Trust is essential in human-robot interactions, and in times where machines are yet to be fully reliable, it is important to study how robotic hardware faults can affect the human counterpart. This experiment builds on a previous research that studied trust changes in a game-like scenario with the humanoid robot iCub. Several robot hardware failures (validated in another online study) were introduced in order to measure changes in trust due to the unreliability of the iCub. A total of 68 participants took part in this study. For half of them, the robot adopted a transparent approach, explaining each failure after it happened. Participants' behaviour was also compared to the 61 participants that played the same game with a fully reliable robot in the previous study. Against all expectations, introducing manifest hardware failures does not seem to significantly affect trust, while transparency mainly deteriorates the quality of interaction with the robot.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it