Safe Vision Language Action Models via Barrier Enhanced Flow Matching

Abstract:This article presents a modular inference framework that integrates Flow Matching generative models with formal Control Barrier Function (CBF) safety guarantees. Unlike existing methods that apply external safety filters to a model's final output, our approach modifies the Flow Matching denoising process within the model to inherently generate safe trajectories. By employing a smooth Log-Sum-Exponential aggregate barrier, we enforce safety over entire action chunks. This aggregate barrier ensures a minimal increase in computational overhead and does not alter the semantic intent of the model. We show that, within the proposed framework, the 2-Wasserstein distance between the generated distribution and the target distribution remains bounded. Our method eliminates the need for safety-specific datasets or costly model retraining, providing a versatile solution for safe inference. We validate the approach on two robotic manipulation platforms and a 2D navigation benchmark, verifying that our framework achieves reliable safety without degrading the success rate of the model.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2607.29569 [cs.RO]

(or arXiv:2607.29569v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.29569 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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