We present a novel framework for representation learning that builds a low-dimensional latent dynamical model from high-dimensional sequential raw data, e.g., video. The framework builds upon recent advances in the amortized inference that constructs a fully-differentiable network, and takes advantage of the duality between control and inference to solve the intractable inference problem using the path integral control approach. We also present the efficient planning method that exploits the learned low-dimensional latent dynamics.