Practicality-Based Probabilistic Roadmaps Method
Why this work is in the frame
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Bibliographic record
Abstract
Probabilistic roadmap methods (PRMs) are a commonly used approach to path planning problems in a high-dimensional search space. Although PRMs can often find a solution to solving the path finding problem the solutions are often not practical in that they can cause the device to flail around or to pass very close to obstacles in the environment. This paper presents a variant of PRMs that addresses the practicality problem of the paths found by the planner. A simple and general sample adjustment method is developed, which adjusts the randomly generated nodes that make up the PRM within their local neighborhood to satisfy soft constraints required by the problem. The resulting roadmap can then be used to generate more practical paths. The approach is general and can be adapted to path planning problems with different practical requirements.
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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 | 0.001 |
| 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 it