# Neural Control Barrier Functions for Safe Navigation

arXiv: https://arxiv.org/abs/2407.19907 · HTML: https://mlsanity.com/paper/2407.19907/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2024.

## Built on by (most cited first, 9 of 9)

- [SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions](https://mlsanity.com/paper/2509.24243/) (2025)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation](https://mlsanity.com/paper/2504.21643/) (2025)
- [T-CBF: Traversability-based Control Barrier Function to Navigate Vertically Challenging Terrain](https://mlsanity.com/paper/2503.06083/) (2025)
- [Safety Certification in the Latent space using Control Barrier Functions and World Models](https://mlsanity.com/paper/2507.13871/) (2025)
- [ORN-CBF: Learning Observation-conditioned Residual Neural Control Barrier Functions via Hypernetworks](https://mlsanity.com/paper/2509.16614/) (2025)
- [GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning](https://mlsanity.com/paper/2603.04659/) (2026)
- [Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation](https://mlsanity.com/paper/2607.13938/) (2026)
- [Distilling Privileged Control Barrier Functions into RGB-Only Safety Filters for Dynamic Visual Navigation](https://mlsanity.com/paper/2609.36520/) (2026)

## Abstract (arXiv)

Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the synthesis of Control Barrier Functions (CBFs) through data for safe navigation in unknown environments. A novel methodology to jointly learn CBFs and corresponding safe controllers, in simulation, inspired by the State Dependent Riccati Equation (SDRE) is proposed. The CBF is used to obtain admissible commands from any nominal, possibly unsafe controller. An approach to apply the CBF inside a safety filter without the need for a consistent map or position estimate is developed. Subsequently, the resulting reactive safety filter is deployed on a multirotor platform integrating a LiDAR sensor both in simulation and real-world experiments.