Handling uncertainty is a crucial capability for autonomous robots. The Partially Observable Markov Decision Process (POMDP) provides a robust framework for this capability. Despite significant advancements in POMDP-based planning, its application to real-world problems is often limited by the challenges of obtaining accurate POMDP models.
We introduce VOiLA (Vectorized Online planning wIth Learned diffusion model for POMDP Agents), a framework that learns task-agnostic POMDP models for online planning under uncertainty.
VOiLA learns transition and observation samplers using conditional diffusion models and learns 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 the Vectorized Online POMDP Planner (VOPP), designed to leverage GPU parallelization.
Experimental results indicate that the distillation strategy reduces sampling costs by nearly three orders of magnitude, making learned generative POMDP models practical for online planning. Evaluation of VOiLA on three benchmark problems shows it achieves equal or better performance than the Recurrent Soft Actor Critic while using less than 10% of the training data, and generalizes much better to unseen environment configurations. Physical robot evaluation indicates that VOiLA uses models learned solely from simulated data to generate a policy that successfully accomplishes the task in 10 out of 10 runs.
Blogger's Review: VOiLA significantly enhances the adaptability and efficiency of POMDPs in dynamic environments by incorporating learned diffusion models. This approach not only reduces computational costs but also improves generalization in real-world settings, opening new possibilities for the practical application of autonomous robotics. Future research could explore optimizing this framework for more complex tasks.