ORN-CBF: Learning Observation-conditioned Residual Neural Control Barrier Functions via Hypernetworks

• URL: http://arxiv.org/abs/2509.16614v2
• Date: Mon, 13 Oct 2025 15:32:55 GMT
• Title: ORN-CBF: Learning Observation-conditioned Residual Neural Control Barrier Functions via Hypernetworks
• Authors: Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig,
• Abstract summary: Control barrier functions (CBFs) have been demonstrated as an effective method for safety-critical control of autonomous systems.<n>We propose observation-conditioned neural CBFs based on Hamilton-Jacobi (HJ) reachability analysis.
• Score: 2.7458654644827134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Control barrier functions (CBFs) have been demonstrated as an effective method for safety-critical control of autonomous systems. Although CBFs are simple to deploy, their design remains challenging, motivating the development of learning-based approaches. Yet, issues such as suboptimal safe sets, applicability in partially observable environments, and lack of rigorous safety guarantees persist. In this work, we propose observation-conditioned neural CBFs based on Hamilton-Jacobi (HJ) reachability analysis, which approximately recover the maximal safe sets. We exploit certain mathematical properties of the HJ value function, ensuring that the predicted safe set never intersects with the observed failure set. Moreover, we leverage a hypernetwork-based architecture that is particularly suitable for the design of observation-conditioned safety filters. The proposed method is examined both in simulation and hardware experiments for a ground robot and a quadcopter. The results show improved success rates and generalization to out-of-domain environments compared to the baselines.

Related papers

• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning [1.7235805533661985]
  We train a constraint function that classifies the states of the system under consideration to safe, i.e., belong to a controlled forward invariant set.<n>We then use that function to label a new set of simulated trajectories to train our neural CBF.<n>We empirically evaluate our approach in four different environments, demonstrating that it outperforms existing baselines.
  arXiv  Detail & Related papers  (2025-10-24T15:20:34Z)
• CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions [2.092779643426281]
  Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers.<n>Recent efforts have explored learning-based methods, such as neural CBFs, to address this issue.<n>We propose a novel framework that leverages split-conformal prediction to generate formally verified neural CBFs.
  arXiv  Detail & Related papers  (2025-03-18T10:01:06Z)
• Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions [26.692688699287213]
  Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems.<n> gaps between the RoM and FoM can result in safety violations.<n>This paper introduces emphpredictive CBFs to address this gap by leveraging rollouts of the FoM to define a predictive robustness term.
  arXiv  Detail & Related papers  (2024-12-05T23:05:25Z)
• Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions [58.19198103790931]
  This paper addresses the problem of safety-critical control for non-affine control systems. It has been shown that optimizing quadratic costs subject to state and control constraints can be sub-optimally reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs) We incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems.
  arXiv  Detail & Related papers  (2023-09-06T05:35:48Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety [7.921648699199647]
  We propose a non-parametric approach for online synthesis of CBFs using Gaussian Processes (GPs) GPs have favorable properties, in addition to being non-parametric, such as analytical tractability and robust uncertainty estimation. We validate our approach experimentally on a quad by demonstrating safe control for fixed but arbitrary safe sets.
  arXiv  Detail & Related papers  (2022-03-29T12:21:28Z)
• Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations [50.37808220291108]
  This paper addresses learning safe output feedback control laws from partial observations of expert demonstrations. We first propose robust output control barrier functions (ROCBFs) as a means to guarantee safety. We then formulate an optimization problem to learn ROCBFs from expert demonstrations that exhibit safe system behavior.
  arXiv  Detail & Related papers  (2021-11-18T23:21:00Z)
• Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty [77.18483084440182]
  Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
  arXiv  Detail & Related papers  (2021-06-13T23:08:49Z)
• Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
  We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs) We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system. To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
  arXiv  Detail & Related papers  (2020-04-07T12:29:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.