DiffPF: Differentiable Particle Filtering With Generative Sampling via Conditional Diffusion Models

This paper proposes DiffPF, adifferentiableparticle filter that leveragesdiffusionmodels for state estimation in dynamic systems. Unlike conventional differentiable particle filters, which require importance weighting and typically rely on predefined or low-capacity proposal distributions, DiffPF learns a flexible posterior sampler by conditioning a diffusion model on predicted particles and the current observation. This enables accurate, equally-weighted sampling from complex, high-dimensional, and multimodal filtering distributions. We evaluate DiffPF across a range of scenarios, including both unimodal and highly multimodal distributions, and test it on simulated as well as real-world tasks, where it consistently outperforms existing filtering baselines. In particular, DiffPF achieves a 90.3% improvement in estimation accuracy on a highly multimodal global localization benchmark, and a nearly 50% improvement on the real-world robotic manipulation benchmark, compared to state-of-the-art differentiable filters. To the best of our knowledge, DiffPF is the first method to integrate conditional diffusion models into particle filtering, enabling high-quality posterior sampling that produces more informative particles and significantly improves state estimation. The code is available athttps://github.com/ZiyuNUS/DiffPF.