# Parameterized Barrier Functions to Guarantee Safety under Uncertainty

arXiv: https://arxiv.org/abs/2303.11385 · HTML: https://mlsanity.com/paper/2303.11385/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning](https://mlsanity.com/paper/2604.13192/) (2026)
- [Probabilistically safe and efficient model-based reinforcement learning](https://mlsanity.com/paper/2504.00626/) (2025)

## Abstract (arXiv)

Deploying safety-critical controllers in practice necessitates the ability to modulate uncertainties in control systems. In this context, robust control barrier functions -- in a variety of forms -- have been used to obtain safety guarantees for uncertain systems. Yet the differing types of uncertainty experienced in practice have resulted in a fractured landscape of robustification -- with a variety of instantiations depending on the structure of the uncertainty. This paper proposes a framework for generalizing these variations into a single form: parameterized barrier functions (PBFs), which yield safety guarantees for a wide spectrum of uncertainty types. This leads to controllers that enforce robust safety guarantees while their conservativeness scales by the parameterization. To illustrate the generality of this approach, we show that input-to-state safety (ISSf) is a special case of the PBF framework, whereby improved safety guarantees can be given relative to ISSf.