Tangible Robotic Fleet Control
Bibliographic record
Abstract
The use of multi-robot teams for field missions is increasing in number and scope, requiring command and control interfaces to be adapted to the operator's needs. Instead of working on interaction modalities that emerge from the engineering realm (screen, gestures or voice), we look at how the humanitarian and military logistics teams collaborate: using physical maps. In this demo, we present our command center, which consists of a swarm of small tabletop robots used to visualize and control a fleet of flying robots. To ensure the scalabilty and robustness of our control system, we leverage decentralized behaviors written in a swarm-specific programming language. We set an example scenario, where the operator must command the fleet to search an area for simulated features of interest using the tabletop robots over a map. The actions of the operator send the flying robots to individual waypoint targets. Meanwhile, the command center monitors the operator: if he is not alone, he may lack focus, and the fleet thus switches to an autonomous deployment mode until the operator's full attention is back.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".