Title: On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters. Abstract: In many robotic applications, it is crucial to maintain a belief about the state of 
a system, like the location of a robot or the pose of an object.
These state estimates serve as input for planning and decision making and 
provide feedback during task execution. 
Recursive Bayesian Filtering algorithms address the state estimation problem,
but they require a model of the process dynamics and the sensory observations as well as 
noise estimates that quantify the accuracy of these models. 
Recently, multiple works have demonstrated that the process and sensor models can be 
learned by end-to-end training through differentiable versions of Recursive Filtering methods.
However, even if the predictive models are known, finding suitable noise models 
remains challenging. Therefore, many practical applications rely on very simplistic noise 
models. 
Our hypothesis is that end-to-end training through differentiable Bayesian 
Filters enables us to learn more complex heteroscedastic noise models for
the system dynamics. We evaluate learning such models with different types of 
filtering algorithms and on two different robotic tasks. Our experiments show that especially 
for sampling-based filters like the Particle Filter, learning heteroscedastic noise 
models can drastically improve the tracking performance in comparison to using 
constant noise models.