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Found 4 papers, 2 papers with code
How to Train Your Differentiable Filter
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.
Accurate Vision-based Manipulation through Contact Reasoning
Planning contact interactions is one of the core challenges of many robotic tasks.
On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters
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.
Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data
In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds.
