SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions
This addresses safety-critical planning in robotics and autonomous systems, offering a novel integration for certified safety with real-time efficiency.
The paper tackled the problem of generative planners based on flow matching lacking formal safety guarantees and producing incomplete paths near constraints, resulting in SafeFlowMatcher achieving faster, smoother, and safer paths than baselines in maze navigation and locomotion benchmarks.
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.