Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics
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 dyna...
| Authors | Seunghwan Jang, Jeongyong Yang, Siddharth Ancha et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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