要旨で参照されている関連研究として、Flow Matching generative models、Control Barrier Function (CBF)、外部安全フィルタを用いた既存手法が挙げられる。次に読むべき論文としては、Flow Matchingの基礎論文（例：Flow Matching for Generative Modeling）や、CBFを用いた安全制御の理論（例：Control Barrier Functions: Theory and Applications）が考えられる。また、ロボット操作における安全な軌道生成の最近の研究も関連する。
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.
