Investigation of Real-Time Task Scheduling on Robot Fleets with Reconfigurable Actuators
Why this work is in the frame
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Bibliographic record
Abstract
Multi-Fleet Scheduling (MFS) is concerned with the issue of assigning tasks to a swarm of mobile robotic agents. In this paper, MFS of tasks using a novel class of mobile agents with reconfigurable modular actuators is proposed and analyzed. MFS is split into two regimes, static and dynamic, where the static regime does not allow real-time reconfiguration of agent actuators. Most pre-existing robotic agents are compatible with the static multi-fleet scheduling (S-MFS) regime, whereas the novel agents being investigated here are capable of using dynamic multi-fleet scheduling (D-MFS). Solutions to both problems are compared, and it is shown that in the worst case scenario, given some set of agents and tasks available at known start times, D-MFS finds the same optimal schedule as S-MFS, whereas D-MFS can be used to find more optimal solutions in some conditions. It is also shown that D-MFS may not always be optimal depending on the arrival of previously unknown a-periodic tasks, as D-MFS provides the optimal schedule for a specific fleet of robots accomplishing a set of tasks for some scheduling algorithm and cost function. By defining and exploring the D-MFS problem, this work paves the way for future investigations in task-prediction, efficient large-scale scheduling algorithms, and novel robot manufacturing capabilities.
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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