ASAP: Arbitrary-Step Safe Planning with MeanFlow and Control Barrier Functions

Generative models have emerged as a powerful paradigm for data-driven robotic planning. Existing flow-matching planners typically rely on multi-step sampling and lack principled mechanisms for enforcing safety constraints at test time. Bridging these limitations is essential for real-world deployment, where planners must support both fast generation and formal safety guarantees. We propose **A**rbitrary-Step **SA**fe **P**lanning (ASAP), a control-augmented MeanFlow framework for safe generation with any number of sampling steps, including one step. ASAP introduces a virtual control input governed by an affine barrier constraint that accounts for the sampling step size, yielding formal safety guarantees of generation for any number of steps. Without retraining, ASAP enforces unseen test-time constraints across navigation, locomotion, manipulation, and dexterous manipulation benchmarks, while planning at least $20\times$ faster than safety-guaranteed baselines on navigation, locomotion, and manipulation, and $5\times$ faster on dexterous manipulation. We further validate ASAP on a real-world robot handover task, where it maintains safety and high task performance under an obstacle, requiring only $0.04$ s per plan and achieving over a $200\times$ speedup over the safe baseline.