BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling
The method treats constrained trajectory generation for diffusion and flow-matching policies as Bayesian posterior sampling. It uses a learned demonstration distribution as a prior and incorporates inference-time costs to tilt the sampling toward feasible, optimal trajectories. By leveraging Feynman–Kac sampling, the authors create a unified, retraining-free sampler for both diffusion and flow policies.