Denoising particle filters train state estimators on individual transitions via score matching, then use the learned denoiser with a dynamics model to approximate Bayesian filtering step-by-step, matching end-to-end baselines while preserving composability.
General in-hand object rotation with vision and touch,
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Denoising Particle Filters: Learning State Estimation with Single-Step Objectives
Denoising particle filters train state estimators on individual transitions via score matching, then use the learned denoiser with a dynamics model to approximate Bayesian filtering step-by-step, matching end-to-end baselines while preserving composability.