  Extending the capabilities of robotics to real-world complex, unstructured
environments requires the need of developing better perception systems while
maintaining low sample complexity. When dealing with high-dimensional state
spaces, current methods are either model-free or model-based based on
reconstruction objectives. The sample inefficiency of the former constitutes a
major barrier for applying them to the real-world. The later, while they
present low sample complexity, they learn latent spaces that need to
reconstruct every single detail of the scene. In real environments, the task
typically just represents a small fraction of the scene. Reconstruction
objectives suffer in such scenarios as they capture all the unnecessary
components. In this work, we present MIRO, an information theoretic
representational learning algorithm for model-based reinforcement learning. We
design a latent space that maximizes the mutual information with the future
information while being able to capture all the information needed for
planning. We show that our approach is more robust than reconstruction
objectives in the presence of distractors and cluttered scenes
