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arXiv · 1506.07365

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

Manuel Watter·Jost Tobias Springenberg·Joschka Boedecker·Martin Riedmiller
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Abstract

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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Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller. 2015-06-24. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. https://arxiv.org/abs/1506.07365

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