Unknown Maze Map with Unknown Coordinates Exploration Through HPHS and CvaR Framework
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
This research focuses on the problem of robots searching for exits with unknown coordinates in a completely unknown environment. An autonomous exploration framework integrating hierarchical division and risk assessment is proposed to deal with the problem. Firstly, the system utilises depth-first search (DFS) in combination with a Visited Map to construct an initial feasible one path. Subsequently, environmental perception and map updating are realised through Occupancy Map - SLAM. At the high-level stage, the HPHS framework is introduced to partition the global environment into coarse-grained regions. Potential exploration targets are screened according to regional accessibility and frontier density. At the local level, frontier clustering is employed to generate candidate points. Simultaneously, the Conditional Value at Risk (CVaR) model is adopted for risk-sensitive selection. This is intended to enhance the robustness and efficiency of exploration. The experimental results demonstrate that this method can effectively avoid local dilemmas in dense unknown mazes while maintaining exploration coherence. Additionally, it surpasses traditional strategies based on A* or simple frontier search in aspects such as coverage rate, path rationality, and convergence speed. This proposed scheme offers a novel solution idea for autonomous exploration in complex, unknown environments.
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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 | 0.001 |
| 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