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The paper combines conditional diffusion models, distilled into feedforward generators, with GPU-parallel online POMDP planning, so an agent can learn a task-agnostic world model and plan with it. The reported gains (up to roughly 1000x lower sampling cost, comparable or better results than Recurrent SAC and DreamerV3 with under 10% of the training data) come from three benchmarks and one physical robot run, so they need independent replication. Readers in robotics and RL should watch whether learned generative world models can replace hand-built POMDP models outside controlled settings.
VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents
arXiv:2606.19729v3 Announce Type: replace-cross 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2606.19729 [cs.RO]
(or arXiv:2606.19729v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.19729 Focus to learn more arXiv-issued DOI via DataCite
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