Denoising Particle Filters: Learning State Estimation with Single-Step Objectives

ArXi:2602.19651v2 Announce Type: replace-cross Learning-based methods commonly treat state estimation in robotics as a sequence modeling problem. While this paradigm can be effective at maximizing end-to-end performance, models are often difficult to interpret and expensive to train, since