用流匹配生成安全动作轨迹，无需重训或额外数据。
Safe Vision Language Action Models via Barrier Enhanced Flow Matching
- 在流匹配去噪过程内嵌入安全屏障函数
- 保证动作序列整体安全且计算开销小
- 适用于机器人操作与导航，保持原模型成功率
本文提出一种模块化推理框架，将流匹配生成模型与形式化控制屏障函数（CBF）安全保证相结合。不同于现有方法在模型输出后添加外部安全过滤器，本方法通过平滑的Log-Sum-Exponential聚合屏障函数，直接修改流匹配的去噪过程，以内在生成安全轨迹。该聚合屏障确保整个动作块的安全性，计算开销最小且不改变模型语义意图。我们证明，在该框架下，生成分布与目标分布之间的2-Wasserstein距离保持有界。本方法无需专用安全数据集或昂贵的模型重训练，提供了一种通用的可靠安全推理方案。我们在两个机器人抓取平台和一个二维导航基准上验证了该方法，结果表明其在不降低模型成功率的前提下实现了可靠的安全部署。
原文摘要 · Abstract (English)
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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