Movers, Shakers, and Those Who Stand Still
Bibliographic record
Abstract
We designed and evaluated a series of teleoperation interface techniques that aim to draw operator attention while mitigating negative effects of interruption. Monitoring live teleoperation video feeds, for example to search for survivors in search and rescue, can be cognitively taxing, particularly for operators driving multiple robots or monitoring multiple cameras. To reduce workload, emerging computer vision techniques can automatically identify and indicate (cue) salient points of potential interest for the operator. However, it is not clear how to cue such points to a preoccupied operator -- whether cues would be distracting and a hindrance to operators -- and how the design of the cue may impact operator cognitive load, attention drawn, and primary task performance. In this paper, we detail our iterative design process for creating a range of visual attention-grabbing cues that are grounded in psychological literature on human attention, and two formal evaluations that measure attention-grabbing capability and impact on operator performance. Our results show that visually cueing on-screen points of interest does not distract operators, that operators perform poorly without the cues, and detail how particular cue design parameters impact operator cognitive load and task performance. Specifically, full-screen cues can lower cognitive load, but can increase response time; animated cues may improve accuracy, but increase cognitive load. Finally, from this design process we provide tested, and theoretically grounded cues for attention drawing in teleoperation.
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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) | 0.022 | 0.001 |
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".