The Frugal Feeding Problem: Energy-efficient, multi-robot, multi-place rendezvous
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
We consider the problem of finding an energy-efficient route for a service robot to rendezvous with every member of a heterogeneous team of mobile worker robots. We analyze the general and special cases of the problem, finding it to be at least as hard as the travelling salesman problem. Decomposing the problem into two components: (i) an ordering of robot meetings; and (ii) finding an optimal set of meeting places given an ordering, we present useful solutions to part (ii) only. We propose and compare a discrete algorithm for the restricted meeting location case and two numerical algorithms for the continuous case with weighted Euclidean distance energy cost functions. Anticipating future work, we speculate briefly on suitable ordering heuristics and the need for an integrated method
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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