Day 3 and 4 of NIPS main meeting (part 1, part 2). More amazing deep learning results.
Embed to control: a locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, Martin Riedmiller
To implement optimal control in the latent state space, they used iterative Linear-Quadratic-Gaussian control applied directly to video. A gaussian latent state space was decoded from images through a deep variational latent variable model. One step prediction of latent dynamics was modeled to be locally linear where the dynamics matrices were parameterized by a neural network that depends on the current state. A variant of a variational cost that minimizes instantaneous reconstruction, and also KL divergence between predicted latent and the reconstructed latent. Deconvolution network was used, and as can be seen in the [video online], the generated images are a bit blurry, but iLQG control works well.
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