Safe Generative PlanningExpert
Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics
SafeStreamingFlow is a preprint describing a start–goal-conditioned flow matching planner that streams one-step state updates in physical execution time, so safety filtering only needs to act on the step actually executed. In simulations, the safe version reaches 88% success with 4% violations on Maze2D Large (vs 13% success and 71% violations for SafeDiffuser) and runs Hopper safety filtering with 7.30 ms total latency vs 1012.4 ms for SafeDiffuser. The result matters because it brings execution-time safety filters to generative planners without the usual O(H*K) compute and distribution-shift failures.