Replayable Augmented Reality Visualization for Robot Fault Diagnosis: A Comparative Study
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
Abstract Efficient fault diagnosis in autonomous robotic systems is essential for minimizing downtime. This study compares the effectiveness of an Augmented Reality (AR) interface and sensor data replay (featuring a 15-second loop before the fault) in diagnosing common robot faults. In a user study, 24 participants experienced a series of eight staged robot fault scenarios. A tablet-based interface, presenting identical information, was favoured over AR due to its effectiveness and ease of use. Participant feedback highlighted the limitations of the AR interface including the low field of view and blurriness, suggesting potential improvements in future AR headset iterations. While a preference for replayed data emerged, it was not supported uniformly, warranting further research. This study advances the exploration of Augmented Reality in human-robot interaction, emphasizing the crucial role of user-friendly interfaces for efficient robot fault diagnosis.
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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