A reactive planner for multiple robots involved in competitive tasks
Bibliographic record
Abstract
In this article, we propose the concept of dynamic map (DM), which allows to represent the intrinsic kinetic capabilities of a vehicle, and therefore to control its displacements once the geometry of this map is known. This concept is based upon the theory of reachable regions for a dynamic system. The work presented in this paper concerns the aspects of: (1) the construction and representation of the map, (2) the learning by a robot of its own map, and (3) the description of the sensori-motor relation between the visual information received by the robot and its displacement using the DM. The second part of the paper concerns the exploitation by a robot of the information contained in its own DM and that of other robots. The maps are combined in order to define a new distribution of the robot's reachable space, in the context of the task at hand. An optimization algorithm can then be used to find a path that takes into account all robots capabilities. This planning scheme is applied to the case of a single robot that is pursued by one or several other robots and therefore must plan an escape trajectory.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".