A comparison of audible, visual, and multi-modal communication for multi-robot supervision and situational awareness
Bibliographic record
Abstract
Multi-robot supervision becomes increasingly cognitively demanding as the ratio of robots to human supervisors rises, potentially leading to situational awareness (SA) losses and robot system failures. Nonverbal cues have been employed to direct supervisor attention and prevent awareness loss in diverse human-computer interaction (HCI) settings. This paper compares the effects of uni-modal and multi-modal audiovisual nonverbal cues on supervisor SA in a multi-robot supervision task. In a simulation-based navigation scenario, 50 participants monitored a multi-robot mission and responded to supervision requests from the robots. We evaluated supervisor SA using response speed and the situational awareness global assessment technique. Results demonstrate that supervisor awareness hinges on the communication method employed by the robots, with greater significance observed at higher awareness levels and when the robot-to-human ratio is higher. Findings also indicate the utility of sonification mapping in human-multirobot interactions and the benefits of multi-modal cues for sustaining awareness during multi-robot supervision.
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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 | 0.000 | 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 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".