Conformative Filter: A Probabilistic Framework for Localization in Reduced Space
Bibliographic record
Abstract
Algorithmic problem reduction is a fundamental approach to problem solving in many fields, including robotics. To solve a problem using this scheme, we must reduce the problem into another one for which solutions exist. The reduction function, which infers a conformation between the problem and the solution space, plays an important role in solution evaluation and is sometimes used to transform the solutions into the problem domain. We consider robot path planning in the context of algorithmic problem reduction where a reduction can be used to adapt a path (referred to as solution) generated by a human or other subsystem to environmental constraints that may differ from those at plan-generation time. Usually, solving these problems involves estimating the current state in the plan and trying to retrieve the solution. We develop a probabilistic framework for reduction-based path planning where the solutions can be obtained from localization into the plan by exploiting the Markov property. We name it Conformative Filter. The algorithm is an extension of Bayes' filter which tries to search for not only the solutions but also conformation between the environment and the plan. An implementation based on Localization and Expectation-maximization is discussed along with evaluation on navigation tasks using a set of actual hand-drawn maps of simulated environments. The results demonstrate applicability and effectiveness of the algorithm in such tasks and show that the proposed filter results in improved localization when compared with conventional approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".