Pith. sign in

REVIEW 3 cited by

Almost-Sure Safety Guarantees of Stochastic Zero-Control Barrier Functions Do Not Hold

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.02430 v1 pith:LGIYFUJY submitted 2023-12-05 math.OC cs.RO

classification math.OCcs.RO
keywords guaranteessafetystochasticbarrierproofalmostfunctionssure
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The 2021 paper "Control barrier functions for stochastic systems" provides theorems that give almost sure safety guarantees given stochastic zero control barrier function (ZCBF). Unfortunately, both the theorem and its proof is invalid. In this letter, we illustrate on a toy example that the almost sure safety guarantees for stochastic ZCBF do not hold and explain why the proof is flawed. Although stochastic reciprocal barrier functions (RCBF) also uses the same proof technique, we provide a different proof technique that verifies that stochastic RCBFs are indeed safe with probability one. Using the RCBF, we derive a modified ZCBF condition that guarantees safety with probability one. Finally, we provide some discussion on the role of unbounded controls in the almost-sure safety guarantees of RCBFs, and show that the rate of divergence of the ratio of the drift and diffusion is the key for whether a system has almost sure safety guarantees.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Stochastic Neural Control Barrier Functions

    eess.SY 2025-06 reject novelty 6.0 of 10

    A framework for synthesizing and verifying neural control barrier functions for stochastic systems, including new Tanaka-formula-based safety conditions for ReLU networks.

  2. Safety-Critical Control for Discrete-time Stochastic Systems with Flexible Safe Bounds using Affine and Quadratic Control Barrier Functions

    eess.SY 2025-01 conditional novelty 6.0 of 10

    Sufficient conditions and K-step exit-probability bounds are given for stochastic discrete-time control systems using affine and quadratic control barrier functions, including unbounded safe sets.

  3. Provably Safe Generative Sampling with Constricting Barrier Functions

    cs.LG 2026-02 reject novelty 5.0 of 10

    A constricting barrier-function controller steers pretrained flow-based generative samplers into hard safety constraints at sampling time, with a continuous-time invariance proof but only an approximate discrete-time ...

Pith tools