Safe Vision Language Action Models via Barrier Enhanced Flow Matching

• URL: http://arxiv.org/abs/2607.29569v1
• Date: Fri, 31 Jul 2026 15:56:19 GMT
• Title: Safe Vision Language Action Models via Barrier Enhanced Flow Matching
• Abstract summary: This article presents a modular inference framework that integrates Flow Matching generative models with formal Control Barrier Function safety guarantees.<n>By employing a smooth Log-Sum-Exponential aggregate barrier, we enforce safety over entire action chunks.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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