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pith:HIV6RJ7H

pith:2026:HIV6RJ7HTXKCA5TYO376C5I6W2
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Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach

Daniel M. Cherenson, Dimitra Panagou, Haejoon Lee, Taekyung Kim

Backup-based safety filtering reduces distributionally robust certification to a one-dimensional search over switching time.

arxiv:2605.12974 v1 · 2026-05-13 · cs.RO · cs.SY · eess.SY

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Claims

C1strongest claim

we exploit the structure of backup-based safety filtering to reduce safety certification to a one-dimensional search over the switching time between nominal and backup policies. We then develop a sampling-based certification procedure with finite-sample guarantees, where empirical failure probabilities are compared against a Wasserstein-inflated threshold.

C2weakest assumption

The true uncertainty distribution lies within the chosen Wasserstein ambiguity set around the empirical distribution, and the backup policy remains certified safe under the worst-case distribution in that set.

C3one line summary

A backup-based safety filter combined with Wasserstein ambiguity sets reduces probabilistic safety certification for nonlinear systems to a one-dimensional search with finite-sample guarantees.

References

37 extracted · 37 resolved · 1 Pith anchors

[1] Advances in the theory of control barrier func- tions: Addressing practical challenges in safe control synthesis for autonomous and robotic systems, 2024
[2] The safety filter: A unified view of safety-critical control in autonomous systems 2023
[3] Hamilton- jacobi reachability: A brief overview and recent advances, 2017
[4] Control barrier functions: Theory and applications, 2019
[5] Robust safety under stochastic uncertainty with discrete-time control barrier functions 2023

Formal links

2 machine-checked theorem links

Receipt and verification
First computed 2026-05-18T03:09:08.787106Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

3a2be8a7e79dd420767876ffe1751eb69e0a3c8b6c39a9123515f92c0b95f6b0

Aliases

arxiv: 2605.12974 · arxiv_version: 2605.12974v1 · doi: 10.48550/arxiv.2605.12974 · pith_short_12: HIV6RJ7HTXKC · pith_short_16: HIV6RJ7HTXKCA5TY · pith_short_8: HIV6RJ7H
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/HIV6RJ7HTXKCA5TYO376C5I6W2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3a2be8a7e79dd420767876ffe1751eb69e0a3c8b6c39a9123515f92c0b95f6b0
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-13T04:11:58Z",
    "title_canon_sha256": "81e867baa5a69a206152637d0b0c0dd091d31bf0c04c5156dcc6c410035a5d48"
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