A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we present a novel high-performance millirobot ( milli- robot- Toronto), designed to allow for the testing of complex swarm-behaviours, including human–swarm interaction. milli- robot- Toronto, built only with off-the-shelf components, has locomotion, processing and sensing capabilities that significantly improve upon existing designs, while maintaining one of the smallest footprints among current millirobots. As complementary software to this hardware development, herein, we also present a new global swarm-topology estimation algorithm. The method is novel in that it uniquely fuses incomplete location data collected by the individual robots in a distributed manner to optimally estimate the topology of the overall swarm using a centralized computer. It is a generalized technique usable by any swarm comprising robots capable of collecting location estimates of neighbouring robots. Numerous experiments, evaluating the performance of milli- robot- Toronto and the proposed optimal swarm-topology estimation algorithm, are also included.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it