Reducing spatial interference in robot teams by local-investment aggression
Bibliographic record
Abstract
This paper extends and improves upon our previous work on the use of stereotypical aggressive display behavior to reduce interference in robot teams, and thus improve their overall efficiency. We examine a team of robots with no centralized control performing a transportation task in which robots frequently interfere with each other. The robots must work in the same space, so territorial methods are not appropriate. In our method, when robots come into competition for floor space, each selects an aggression level and the competition is resolved in favor of the more aggressive robot. Our recent work showed that choosing aggression proportional to task investment can produce better overall system performance compared to aggression chosen at random. This paper describes a new technique, local investment, for computing an aggression level that performs better than any previous method and relies only on local sensor data. The method is evaluated in a simulation study, and is then shown to be effective in a real-world robot implementation.
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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) | 0.001 | 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".