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We introduce Embed to Control (E2C), a method for model learning and controlof non-linear dynamical systems from raw pixel images. E2C consists of a deepgenerative model, belonging to the family of variational autoencoders, thatlearns to generate image trajectories from a latent space in which the dynamicsis constrained to be locally linear. Our model is derived directly from anoptimal control formulation in latent space, supports long-term prediction ofimage sequences and exhibits strong performance on a variety of complex controlproblems.

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images