Monocle: Interactive detail-in-context using two pan-and-tilt cameras to improve teleoperation effectiveness
Bibliographic record
Abstract
Robot teleoperation, such as for search and rescue, uses multiple specialized cameras (e.g., wide environmental and sharp narrow views) to aid in task awareness. Simple display techniques, such as tiling, require ongoing mental mapping between the views; cameras that pan or tilt exacerbate the problem as the inter-view relationship changes. The detail-in-context technique bypasses this mental mapping requirement by providing a single integrated feed showing all cameras, with detail overlaid within the context. However, how this can be adapted to for robot teleoperation with multiple pan-and-tilt cameras has not yet been demonstrated. We present Monocle, an interactive detail-in-context teleoperation interface that integrates a pan-and-tilt narrow-angle first-person view into a wide-angle behind-robot view; operators can move the Monocle around a scene to obtain more resolution when and where needed. Evaluation results demonstrate Monocle's feasibility and show that it can help operators complete search and rescue tasks more effectively in comparison to simple solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".