Sustainable robot foraging: Adaptive fine-grained multi-robot task allocation for maximum sustainable yield of biological resources
Why this work is in the frame
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Bibliographic record
Abstract
We introduce the concept of Maximum Sustainable Yield (MSY) to the context of autonomous robot foraging. MSY is an optimal approach to the problem of maximizing sustainable foraging where the resources harvested are replenished by logistic growth, e.g. living things. Over-harvesting reduces both the instantaneous resource availability and growth rate, and above some threshold will permanently deplete resources. Under-harvesting is sustainable, but fails to maximally exploit the resources. We describe a system model and use it to determine the optimal allocation of robot work to resource-producing `patches'. We give a practical illustration of a troublesome feature of MSY: it is too sensitive for a fixed allocation to be sustainable in practice. We show how to centrally allocate a number of robots to each patch, and then locally adapt the work rate of each robot to achieve sustainable and near-optimal foraging. This is the first study of robot foraging where the robots' activity modifies the productivity and sustainability of the environment.
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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