Motion Planning of Multiple Agents in Virtual Environments using Coordination Graphs
Why this work is in the frame
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Bibliographic record
Abstract
Motion planning of multiple mobile agents in virtual environments is a very challenging problem, especially if one wants to plan the motions of these agents in real-time. We propose a two layered approach to plan motions of multiple mobile agents in real-time. The mobile agents are moving in a 2-dimensional static environment with open spaces connected to each other by narrow corridors. The global path of each agent is computed by a decoupled planner during the preprocessing process with minimum delay. Each agent’s local path is generated in real-time by combining steering behaviors and a new, principled and efficient AI technique for decision making and planning cooperative multi-agent dynamic systems, Coordination Graph (CG). With CG, we can not only avoid deadlocks in narrow corridors, but also achieve more complicated behavior such as leader-and-followers behavior. We show, via some preliminary examples, real-time performance of our approach, for instance, several robots avoiding deadlocks and successfully navigating a corridor.
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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