2015
NIPS
NeurIPS 2015
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
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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Topic Pioneer
— Model-Based RL
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning
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Keyword Pioneer
— variational autoencoder
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Hot Topic Early Bird
— optimal control
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio