Risk-Aware Robotics: Tail Risk Measures in Planning, Control, and Verification [Focus on Education]
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
Often, control theorists and roboticists expect systems to function as reliably and predictably as the equations we use to represent them. Sadly, reality is often more random than our equations. For example, take a robot navigating in two similar but unstructured environments. Random perturbations in terrain and scenery could cause the robot to take wildly different paths. In another example, take a perfectly orchestrated robotic swarm that finds itself in dissonance moments later due to network connectivity going down and package loss. Such randomness arises because our equations are imperfect models of reality. So, perhaps we should find a way to account for such randomness in our equations themselves. This article delves into how tail risk measures—formal mathematical concepts of risk traditionally used in the financial community—facilitate accounting for this randomness in planning, control, and verification. The exposition to follow both defines these measures and includes multiple examples of their use in prescribing risk-aware control across all levels of the modern control stack. Finally, we end with a brief survey of existing and open problems in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
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