PA-FaSTrack: Planner-Aware Real-Time Guaranteed Safe Planning
Why this work is in the frame
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Bibliographic record
Abstract
Guaranteed safe online trajectory planning is becoming an increasingly important topic of robotic research, due to the need to react quickly in unknown environments. However, as a result of modelling mismatch, some error during trajectory tracking is inevitable. In this paper, we present Planner-Aware FaSTrack, or PA-FaSTrack, which provides guaranteed Tracking Error Bounds (TEBs) by solving a Hamilton-Jacobi (HJ) variational inequality in the tracking error space. PA-FaSTrack improves upon the state-of-the-art method, FaSTrack [1], by accounting for motion primitives implied by the planning algorithm in the problem formulation. Our method provides a sequence of TEBs, with each TEB corresponding to a segment of the planned path. We also propose necessary modifications to real time tree based planning algorithms in order to make them compatible with the provided TEB sequence. By integrating planning and tracking more closely together, we greatly decrease the degree of conservatism compared to the original FaSTrack, allowing the autonomous system to navigate safely through much narrower spaces. We demonstrate our method using two representative dynamical systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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