# SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions

arXiv: https://arxiv.org/abs/2504.08661 · HTML: https://mlsanity.com/paper/2504.08661/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Built on by (most cited first, 13 of 13)

- [UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning](https://mlsanity.com/paper/2506.02955/) (2025)
- [Action Hallucination in Generative Vision-Language-Action Models](https://mlsanity.com/paper/2602.06339/) (2026)
- [Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation](https://mlsanity.com/paper/2603.05754/) (2026)
- [From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation](https://mlsanity.com/paper/2603.09415/) (2026)
- [Neuro-Symbolic Safety Guidance for Vision-Language-Action Models via Constrained Flow Matching](https://mlsanity.com/paper/2607.01378/) (2026)
- [DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising](https://mlsanity.com/paper/2606.15359/) (2026)
- [Safe Vision Language Action Models via Barrier Enhanced Flow Matching](https://mlsanity.com/paper/2607.29569/) (2026)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [PC-Diffuser: Path-Consistent Capsule CBF Safety Filtering for Diffusion-Based Trajectory Planner](https://mlsanity.com/paper/2603.10330/) (2026)
- [Guided Discovery of New Behaviors using Diffusion Policies](https://mlsanity.com/paper/2606.08743/) (2026)
- [Constrained Flow Matching via Lagrangian Dual Flows](https://mlsanity.com/paper/2607.04513/) (2026)
- [Multi-Agent Flow Matching with Decoupled Generative Guidance](https://mlsanity.com/paper/2609.38133/) (2026)
- [Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance](https://mlsanity.com/paper/2610.17799/) (2026)

## Abstract (arXiv)

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.