arXiv:2606.19729v3 Announce Type: replace Abstract: Planning under uncertainty is an essential capability for autonomous robots. The Partially Observable Markov Decision Process (POMDP) provides a powerful framework for such a capability. Although POMDP-based planning has advanced significantly, its application to real-world problems is often limited by the difficulty of obtaining faithful POMDP models. We present Vectorized Online planning wIth Learned diffusion model for POMDP Agents (VOiLA), a framework that learns task-agnostic POMDP world models for online planning under uncertainty. Specifically, VOiLA learns transition and observation samplers using conditional diffusion models and observation-likelihood models for particle-based belief updates. To enable efficient online planning, the diffusion samplers are distilled into compact feedforward generators and integrated with Vectorized Online POMDP Planner (VOPP), an online POMDP planner designed to leverage GPU parallelization. Experimental results indicate the distillation strategy reduces sampling cost by up to nearly three orders of magnitude, making learned generative POMDP models practical for online planning. Evaluation of VOiLA on three benchmark problems indicates that VOiLA achieves equal or better performance than current reinforcement learning (RL) methods for POMDPs, such as Recurrent Soft Actor Critic and DreamerV3, while using less than 10% of the training data, and generalizes much better to unseen environment configurations. In a physical robot experiment, VOiLA accomplishes the task in 10 of 10 runs while using models learned purely from simulated data for online planning.