SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions

• URL: http://arxiv.org/abs/2509.24243v2
• Date: Mon, 06 Oct 2025 04:13:09 GMT
• Title: SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions
• Abstract summary: We present SafeFlowMatcher, a planning framework that couples flow matching (FM) with control barrier functions (CBFs) to achieve both real-time efficiency and certified safety.<n>We prove a barrier certificate for the resulting flow system, establishing forward invariance of a robust safe set and finite-time convergence to the safe set.<n>Across maze navigation and benchmarks, SafeFlowMatcher attains faster, smoother, and safer paths than diffusion- and FM-based baselines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative planners based on flow matching (FM) can produce high-quality paths in one or a few ODE steps, but their sampling dynamics offer no formal safety guarantees and can yield incomplete paths near constraints. We present SafeFlowMatcher, a planning framework that couples FM with control barrier functions (CBFs) to achieve both real-time efficiency and certified safety. SafeFlowMatcher uses a two-phase prediction-correction (PC) integrator: (i) a prediction phase integrates the learned FM once (or a few steps) to obtain a candidate path without intervention; (ii) a correction phase refines this path with a vanishing time-scaled vector field and a CBF-based quadratic program that minimally perturbs the vector field. We prove a barrier certificate for the resulting flow system, establishing forward invariance of a robust safe set and finite-time convergence to the safe set. By enforcing safety only on the executed path (rather than on all intermediate latent paths), SafeFlowMatcher avoids distributional drift and mitigates local trap problems. Across maze navigation and locomotion benchmarks, SafeFlowMatcher attains faster, smoother, and safer paths than diffusion- and FM-based baselines. Extensive ablations corroborate the contributions of the PC integrator and the barrier certificate.

Related papers

• Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC [52.33021019321273]
  Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems.<n>Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high overhead and accumulated errors.<n>We propose an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios.
  arXiv  Detail & Related papers  (2026-09-08T20:03:33Z)
• Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
  This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
  arXiv  Detail & Related papers  (2026-07-22T16:37:05Z)
• From Prediction Uncertainty to Conformalized Distance Fields for Safe Motion Planning [9.50741434664352]
  Existing conformal approaches conformalize a scalar score that aggregates per-obstacle prediction errors.<n>We instead conformalize the entire predicted distance field at once.<n>This functional conformal prediction framework yields a distribution-free, field-level lower bound.
  arXiv  Detail & Related papers  (2026-07-01T11:02:51Z)
• Predicted-Flow Control Barrier Functions for Real-Time Safe Optimal Control [0.0]
  Control barrier functions (CBFs) provide real-time safety guarantees through pointwise conditions on the state.<n>This article introduces predicted-flow control barrier functions (P-CBFs)<n>For safety, a P-CBF can certify that the predicted flow is in a safe set over the entire prediction horizon.
  arXiv  Detail & Related papers  (2026-05-29T19:27:24Z)
• Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters [18.386652442201665]
  This paper presents a general end-to-end framework for constructing robust and reliable layered safety filters.<n>Given a robot-centric point cloud, we begin by constructing an occupancy map which is used to synthesize a Poisson safety function (PSF)<n>The PSF is employed as a control barrier function (CBF) within two distinct safety filtering stages.
  arXiv  Detail & Related papers  (2026-02-27T22:19:45Z)
• 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)
• Conditional Flow Matching for Continuous Anomaly Detection in Autonomous Driving on a Manifold-Aware Spectral Space [0.0]
  We present Deep-Flow, an unsupervised framework for safety-critical anomaly detection.<n>Unlike standard generative approaches that operate in unstable, high-dimensional coordinate spaces, Deep-Flow constrains the generative process to a low-rank spectral manifold.<n>This ensures kinematic smoothness by design and enables the computation of numerically stable, deterministic log-likelihood estimation.
  arXiv  Detail & Related papers  (2026-02-19T18:10:16Z)
• Building a Foundational Guardrail for General Agentic Systems via Synthetic Data [76.18834864749606]
  LLM agents can plan multi-step tasks, intervening at the planning stage-before any action is executed-is often the safest way to prevent harm.<n>Existing guardrails mostly operate post-execution, which is difficult to scale and leaves little room for controllable supervision at the plan level.<n>We introduce AuraGen, a controllable engine that synthesizes benign trajectories, injects category-labeled risks with difficulty, and filters outputs via an automated reward model.
  arXiv  Detail & Related papers  (2025-10-10T18:42:32Z)
• Efficient Switchable Safety Control in LLMs via Magic-Token-Guided Co-Training [1.5349686675266894]
  Current methods for content safety in Large Language Models (LLMs) rely on multi-stage training pipelines.<n>We propose a unified co-training framework that efficiently integrates multiple safety behaviors.<n>We show that our method matches the safety alignment quality of SFT+DPO, with our 8B model notably surpassing DeepSeek-R1 (671B) in safety performance.
  arXiv  Detail & Related papers  (2025-08-12T02:39:33Z)
• SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation [67.22657932549723]
  We introduce SafePath, a framework that augments Large Language Models (LLMs) with formal safety guarantees.<n>In the first stage, we use an LLM that generates a set of diverse candidate paths, exploring possible trajectories based on agent behaviors and environmental cues.<n>In the second stage, SafePath filters out high-risk trajectories while guaranteeing at least one safe option is included with a user-defined probability.<n>In the final stage, our approach selects the path with the lowest expected collision risk when uncertainty is low or delegates control to a human when uncertainty is high.
  arXiv  Detail & Related papers  (2025-05-14T14:28:24Z)
• 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)
• Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation [5.62479170374811]
  Control Barrier Functions (CBFs) provide an elegant framework for constraining nonlinear control system dynamics. We introduce a novel self-supervised learning framework to comprehensively address these challenges. We validate our approach on a 2D double integrator (DI) system and a 7D fixed-wing aircraft system.
  arXiv  Detail & Related papers  (2024-01-11T02:51:49Z)
• Wasserstein Distributionally Robust Control Barrier Function using Conditional Value-at-Risk with Differentiable Convex Programming [4.825619788907192]
  Control Barrier functions (CBFs) have attracted extensive attention for designing safe controllers for real-world safety-critical systems. We present distributional robust CBF to achieve resilience under distributional shift. We also provide an approximate variant of DR-CBF for higher-order systems.
  arXiv  Detail & Related papers  (2023-09-15T18:45:09Z)
• 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)
• 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)
• 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)
• 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.