Learning an interpretable logic monitor for risk-aware and socially-compliant trajectory planning
Bibliographic record
Abstract
In order to plan robot behaviors effectively in the real world, it is often necessary to consider risks and learn from humans in dealing with them. We posit that humans manage risks by taking into consideration the nuances of the task that are specific to the current location and social context. We leverage past time signal temporal logic (ptSTL) formulas for forming compact, human-interpretable notions of risk. We introduce LogicRiskNet , a logic monitor constructed from ptSTL formulas that provide parameterized risk metrics and allow risk parameters to be learned from demonstration data capturing human behavior in risky situations. LogicRiskNet can be used to reason about environment agent behaviors and be incorporated into the controlled agent’s planner. To achieve human-like risk awareness, we explore an online adaptation mechanism that allows LogicRiskNet to update its parameters online in order to adhere to the aggregate behavior of its surrounding agents, while also benefitting from prior experience learned offline from large-scale datasets. We integrate LogicRiskNet in an inverse optimal control (IOC) framework and evaluate it on a real-world driving dataset. We show that our approach learns to generate trajectory plans that mimic the expert’s risk handling behaviors from offline demonstrations and adapts online to surrounding traffic at deployment time .
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".