  The ability to predict and plan into the future is fundamental for agents
acting in the world. To reach a faraway goal, we predict trajectories at
multiple timescales, first devising a coarse plan towards the goal and then
gradually filling in details. In contrast, current learning approaches for
visual prediction and planning fail on long-horizon tasks as they generate
predictions (1) without considering goal information, and (2) at the finest
temporal resolution, one step at a time. In this work we propose a framework
for visual prediction and planning that is able to overcome both of these
limitations. First, we formulate the problem of predicting towards a goal and
propose the corresponding class of latent space goal-conditioned predictors
(GCPs). GCPs significantly improve planning efficiency by constraining the
search space to only those trajectories that reach the goal. Further, we show
how GCPs can be naturally formulated as hierarchical models that, given two
observations, predict an observation between them, and by recursively
subdividing each part of the trajectory generate complete sequences. This
divide-and-conquer strategy is effective at long-term prediction, and enables
us to design an effective hierarchical planning algorithm that optimizes
trajectories in a coarse-to-fine manner. We show that by using both
goal-conditioning and hierarchical prediction, GCPs enable us to solve visual
planning tasks with much longer horizon than previously possible.
