The Robust Canadian Traveler Problem Applied to Robot Routing
Why this work is in the frame
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Bibliographic record
Abstract
The stochastic Canadian Traveler Problem (CTP), which finds application in robot route selection under uncertainty, aims to find the traversal policy with the minimum expected cost. This paper extends the CTP to what we call the Robust Canadian Traveler Problem (RCTP), in which the variability of the policy cost is also part of the evaluation criteria. An optimal (offline) algorithm and an approximate (online) algorithm are then proposed to compute the policy that has a good balance of both mean and variation of the traversal cost. The benefit of the proposed framework versus traditional approaches is shown by doing simulations in randomly generated worlds as well as on a map of 5 km of paths built from robot field trials. Specifically, the RCTP framework is able to search for sub-optimal policy alternatives with significantly lower worst-case cost and less computational time compared to the optimal policy, but with little sacrifice on the expected cost.
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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 | 0.000 |
| 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 | 0.001 |
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