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This paper introduces SafeStreamingFlow, a novel generative planner for long-horizon trajectory synthesis that addresses the challenges of real-world deployment by aligning sampling and execution dynamics. By sequentially integrating a learned state vector field with hierarchical state prediction and enforcing safety constraints only on the executed step via high-order control barrier functions, the method overcomes the computational intensity and distribution shift of prior approaches. SafeStreamingFlow demonstrates reduced planning latency and improved safety across various benchmarks while maintaining competitive goal-reaching success.
Generative planners can now achieve real-time safety and replanning by aligning diffusion sampling with actual system dynamics, bypassing the need for full trajectory generation and costly iterative safety checks.
Generative planners based on diffusion/flow matching can learn to synthesize long-horizon trajectories from demonstrations. However, real-world deployment requires (i) enforcing safety constraints during execution and (ii) tight online replanning at fast execution rates. Prior safe diffusion/flow planners generate the agent's full trajectory at once, while repeatedly perturbing intermediate states to satisfy safety constraints. This approach is not only computationally intensive, but also introduces distribution shift since the learned sampling dynamics is distinct from the system's execution dynamics. We propose SafeStreamingFlow, a goal-conditioned planner that aligns flow sampling dynamics with execution dynamics by sequentially integrating a learned state vector field with hierarchical state prediction. Importantly, we need to enforce safety constraints only for the executed step via high order control barrier functions. Across navigation, racing, and locomotion benchmarks, SafeStreamingFlow reduces planning latency and improves safety compared to existing methods, while maintaining competitive goal-reaching success.