Backprop KF: Learning Discriminative Deterministic State Estimators

@inproceedings{Haarnoja2016BackpropKL,
  title={Backprop KF: Learning Discriminative Deterministic State Estimators},
  author={Tuomas Haarnoja and Anurag Ajay and Sergey Levine and Pieter Abbeel},
  booktitle={NIPS},
  year={2016}
}
Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles. However, generative models have limited capacity to handle rich sensory observations, such as camera images, since they must model the entire distribution over sensor readings. Discriminative models do not suffer from this limitation, but are typically more complex to train as latent variable models for state estimation. We present… CONTINUE READING
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