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Poster
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Manuel Watter · Jost Springenberg · Joschka Boedecker · Martin Riedmiller

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #20
in Poster Session »

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems.

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Author Information

Manuel Watter (University of Freiburg)
Jost Springenberg (University of Freiburg)
Joschka Boedecker (University of Freiburg)
Martin Riedmiller (Google DeepMind)

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