Multi-robot target pursuit: towards an opportunistic control architecture
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
This paper proposes an opportunistic control architecture to action selection in multi-robot, target pursuit problem. Proposed architecture views the problem of action selection in multi-robot environment as an instance of distributed probabilistic inference over the set of robotic agents' available actions, there by constructing a joint probability distribution from local evidence (e.g. robotic agents' respective views of the problem)and the higher level system task perspective. In present work, no explicit inter-robot communication is necessary, instead, robots attain necessary information such as other group members' as well as target's relative positioning information via communicating with a third party agent, the mediation unit. A novel rating system embedded within the control mechanism enables the system to not only determine robots' own action ranking (e.g. default rating) but also incorporates a tactical rating (e.g. opportunisitc rating) at group level. Performance of the opportunistic controller is evaluated in a multi-robot pursuit scenario so as to determine the utility of the proposed sub-ratings system. The analysis of the system performance has been carried out in two parts viz. presence or absence of mediator and absence of opportunistic sub-rating.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.003 | 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