IQ-RRT*: a path planning algorithm based on informed-RRT* and quick-RRT*
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
Optimal path planning algorithms such as the RRT* and its variants seek to generate the best feasible path from an initial state to a goal state in the least possible time. Prior work on RRT* has focused on improving the convergence rate of the algorithm while keeping its computational complexity unchanged. Informed-RRT* and quick-RRT* are two such variants that, in certain scenarios, converge to the optimal path faster than RRT* does. This work focuses on the novel addition of informed sampling to quick-RRT* to enhance its convergence rate. The resultant algorithm provides initial solutions with costs comparable to quick-RRT* and convergence rates at par with quick-RRT* in the worst case. The authors have concluded that this new algorithm, named IQ-RRT*, outperforms informed-RRT* and quick-RRT* in a multitude of scenarios. IQ-RRT*, unlike quick-RRT*, is a faster alternative to informed-RRT* even in cluttered environments and mazes with long corridors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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