Complete real-time path planning during sensor-based discovery
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
Sensor-based discovery path planning is problematic because the path needs to be continually recomputed as new information is discovered. A process-based client-server approach is presented that permits concurrent sensor-based map updates, robot localization corrections, as well as concurrent path computation and execution. A harmonic function is constantly recomputed using an iteration kernel on an occupancy-grid representation of what is known about the world at any current time. The path produced (i.e., by steepest gradient descent on the harmonic function) is optimal in the sense of minimizing the distance to the goal as well as minimizing the hitting probability. This helps alleviate the influence of uncertainty on path planning. In addition, the computation time for generating the path is insignificant provided that the harmonic function has converged. On a regular grid, the computation of the harmonic function is linear in the total number of grid elements. A quad-tree representation is proposed to help minimize the computation time by reducing the number of grid elements and minimally representing large spaces void of obstacles and goals. The computation time is found to be approximately reduced by a factor of n/sup 2/, where n is the ratio of grid elements in the regular grid divided by the number in the reduced quad representation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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