Auction-based solution for the ordering problem in robotic self-assembly
Why this work is in the frame
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Bibliographic record
Abstract
Autonomous construction is a process that uses robots to build structures. In those, the self-assembly denotes the robotic construction solutions where the robots are used as the structure parts. A common problem in self-assembly strategies is that given a homogeneous group of robots (a swarm), how the next robot to assemble a structure can be selected. Such selection can be even more complex if there are multiple structures being assembled simultaneously by the same group of robots. In this paper, we model the selection of robots as a task assignment problem, and we propose an auction-based method to compute an order of which robots will move to structures being assembled. Our algorithms are validated using mathematical proofs and simulations. The analysis of the results shows that our algorithms outperform a baseline selection method while guaranteeing communication between robots in the swarm. Moreover, our solution is shown to be power efficient, reducing battery consumption while the robot is in an idle state, waiting to be assigned.
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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