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.
authors: Alina Kloss Jeannette Bohg
hIndex: Jeannette Bohg 29