Evolving Autonomous Charging Behavior in a Robot Swarm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Long-term autonomous operation of a robotic swarm in harsh or inaccessible locations will require the ability of the swarm to manage operational details such as battery charging without human intervention. Small robots may not be able to manage their own power internally, via solar cells or fuel cells, but may be dependent on external means to recharge batteries. In this paper we will demonstrate the ability of a simple robotic swarm to use a genetic algorithm (GA) to evolve optimized, behavioral parameters including the ability to determine battery charging behavior. We introduce two new battery parameters to the robots’ central place foraging algorithm (CPFA) and allow the GA to optimize the behaviors such that, on average, no robots are left “dead” due to inadequate battery charge. We also demonstrate that the GA can evolve a successful resource collection strategy while minimizing the number of robot casualties. The additional parameters and behaviors do not alter the original algorithm’s simplicity, ability to run in real-time, and requirement of using only local knowledge.
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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 | 0.000 | 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) | 0.000 | 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