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Recent advances in latent space dynamics model from pixels show promisingprogress in vision-based model predictive control (MPC). However, executing MPCin real time can be challenging due to its intensive computational cost in eachtimestep. We propose to introduce additional learning objectives to enforcethat the learned latent space is proportional derivative controllable. Inexecution time, the simple PD-controller can be applied directly to the latentspace encoded from pixels, to produce simple and effective control to systemswith visual observations. We show that our method outperforms baseline methodsto produce robust goal reaching and trajectory tracking in variousenvironments.

Learn Proportional Derivative Controllable Latent Space from Pixels