用流匹配生成安全轨迹，无需重训即可实时保障机器人运动安全。
SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions
- 将流匹配与控制屏障函数结合，实现轨迹生成时的全程安全约束。
- 在平面导航和7自由度操作任务中，安全性和规划效果优于现有生成式方法。
- 训练后无需重训，部署时可实时动态保障安全，适合复杂动态环境应用。
近年来，生成建模的进步在机器人运动规划中取得显著进展，特别是基于扩散模型和流匹配（FM）的方法能够捕捉复杂的多模态轨迹分布。然而，这些方法通常离线训练，面对新环境约束时表现受限，且缺乏显式安全机制保障部署安全。本文提出安全流匹配（SafeFlow）框架，通过引入流匹配屏障函数（FMBF），确保规划轨迹在整个规划时域内始终处于安全区域。关键优势在于无需重新训练，即可在测试阶段实现免训练的实时安全约束。我们在多种任务上进行了评估，包括平面机器人导航与7自由度操作任务，结果表明其在安全性与规划性能方面均优于当前最先进的生成式规划器。完整资源见项目主页：https://safeflowmatching.github.io。
原文摘要 · Abstract (English)
Recent advances in generative modeling have led to promising results in robot motion planning, particularly through diffusion and flow matching (FM)-based models that capture complex, multimodal trajectory distributions. However, these methods are typically trained offline and remain limited when faced with new environments with constraints, often lacking explicit mechanisms to ensure safety during deployment. In this work, safe flow matching (SafeFlow), a motion planning framework, is proposed for trajectory generation that integrates flow matching with safety guarantees. SafeFlow leverages our proposed flow matching barrier functions (FMBF) to ensure the planned trajectories remain within safe regions across the entire planning horizon. Crucially, our approach enables training-free, real-time safety enforcement at test time, eliminating the need for retraining. We evaluate SafeFlow on a diverse set of tasks, including planar robot navigation and 7-DoF manipulation, demonstrating superior safety and planning performance compared to state-of-the-art generative planners. Comprehensive resources are available on the project website: https://safeflowmatching.github.io.
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