Control Barrier Corridors: From Safety Functions to Safe Sets

• URL: http://arxiv.org/abs/2603.06494v1
• Date: Fri, 06 Mar 2026 17:23:56 GMT
• Title: Control Barrier Corridors: From Safety Functions to Safe Sets
• Abstract summary: Control barrier functions and safe motion corridors are two widely used but technically distinct safety methods.<n>This paper introduces a new notion of control barrier corridors, which unifies these two approaches.<n>We show that individual state safety can be extended locally over control barrier corridors for convex barrier functions.
• Score: 7.396792281840145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe autonomy is a critical requirement and a key enabler for robots to operate safely in unstructured complex environments. Control barrier functions and safe motion corridors are two widely used but technically distinct safety methods, functional and geometric, respectively, for safe motion planning and control. Control barrier functions are applied to the safety filtering of control inputs to limit the decay rate of system safety, whereas safe motion corridors are geometrically constructed to define a local safe zone around the system state for use in motion optimization and reference-governor design. This paper introduces a new notion of control barrier corridors, which unifies these two approaches by converting control barrier functions into local safe goal regions for reference goal selection in feedback control systems. We show, with examples on fully actuated systems, kinematic unicycles, and linear output regulation systems, that individual state safety can be extended locally over control barrier corridors for convex barrier functions, provided the control convergence rate matches the barrier decay rate, highlighting a trade-off between safety and reactiveness. Such safe control barrier corridors enable safely reachable persistent goal selection over continuously changing barrier corridors during system motion, which we demonstrate for verifiably safe and persistent path following in autonomous exploration of unknown environments.

Related papers

• Safety-Critical Whole-Body Control for Humanoid Robots via Input-to-State Safe Control Barrier Functions [6.59514190732471]
  This paper presents a hierarchical safety-critical whole-body control framework for humanoid robots.<n>The proposed architecture integrates a kinematic-level whole-body controller (KinWBC), an ISSf-CBF safety filter, and a dynamic-level whole-body controller (DynWBC)
  arXiv  Detail & Related papers  (2026-05-25T08:02:30Z)
• Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments [0.711144397510333]
  We formulate safety constraints using defender-induced engagement zones.<n>A smooth aggregate safety function is constructed using a log-sum-exp operator.<n>A smooth switching guidance strategy is then developed to coordinate interception and safety objectives.
  arXiv  Detail & Related papers  (2026-03-24T18:49:13Z)
• Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions [18.662098889718497]
  Safe-SAGE is a unified framework that bridges the gap between high-level semantic understanding and low-level safety-critical control.<n>Our framework enables legged robots to safely navigate semantically rich, dynamic environments with context-dependent safety margins.
  arXiv  Detail & Related papers  (2026-03-05T18:59:02Z)
• 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)
• On transferring safety certificates across dynamical systems [2.034258001065168]
  Control barrier functions (CBFs) provide a powerful tool for enforcing safety constraints in control systems.<n>But their direct application to complex, high-dimensional dynamics is often challenging.<n>This paper addresses the problem of transferring safety guarantees between dynamical systems with mismatched dynamics.
  arXiv  Detail & Related papers  (2026-02-03T20:20:08Z)
• UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models [67.91151588917396]
  Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks.<n>We propose UpSafe$circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling.<n>Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.
  arXiv  Detail & Related papers  (2025-10-02T16:43:33Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments [4.788163807490197]
  We propose a Soft Actor-Critic (SAC)-based policy for adapting Control Barrier Function (CBF) constraint parameters at runtime.<n>We demonstrate that our framework effectively adapts CBF constraints, enabling the robot to reach its final goal without compromising safety.
  arXiv  Detail & Related papers  (2025-03-11T14:33:55Z)
• Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints [50.920465513162334]
  We introduce the PCBF algorithm to maximize the inner safe set of dynamical systems under input constraints.<n>We validate its effectiveness through comparison with Hamilton-Jacobi reachability for an inverted pendulum and through simulations on a 12-dimensional quadrotor system.<n>Results show that the PCBF consistently outperforms existing methods, yielding larger safe sets and ensuring safety under input constraints.
  arXiv  Detail & Related papers  (2024-10-05T18:45:19Z)
• CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems [7.77024796789203]
  CaRT is a new hierarchical, distributed architecture to guarantee the safety and robustness of a learning-based motion planning policy. We show that CaRT guarantees safety and the exponentialness of the trajectory tracking error, even under the presence of deterministic and bounded disturbance. We demonstrate the effectiveness of CaRT in several examples of nonlinear motion planning and control problems, including optimal, multi-spacecraft reconfiguration.
  arXiv  Detail & Related papers  (2023-07-13T21:51:29Z)
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments [84.3830478851369]
  We propose a safe reinforcement learning approach that can jointly learn the environment and optimize the control policy. Our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.
  arXiv  Detail & Related papers  (2022-09-29T20:49:25Z)
• BarrierNet: A Safety-Guaranteed Layer for Neural Networks [50.86816322277293]
  BarrierNet allows the safety constraints of a neural controller be adaptable to changing environments. We evaluate them on a series of control problems such as traffic merging and robot navigations in 2D and 3D space.
  arXiv  Detail & Related papers  (2021-11-22T15:38:11Z)

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.