  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 differentiable versions of recursive filtering
algorithms. In this work, we investigate the advantages of differentiable
filters (DFs) over both unstructured learning approaches and manually-tuned
filtering algorithms, and provide practical guidance to researchers interested
in applying such differentiable filters. For this, we implement DFs with four
different underlying filtering algorithms and compare them in extensive
experiments. Specifically, we (i) evaluate different implementation choices and
training approaches, (ii) investigate how well complex models of uncertainty
can be learned in DFs, (iii) evaluate the effect of end-to-end training through
DFs and (iv) compare the DFs among each other and to unstructured LSTM models.
