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How to Train Your Differentiable Filter

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 نشر من قبل Alina Kloss
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
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In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiab



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