  Prospection, the act of predicting the consequences of many possible futures,
is intrinsic to human planning and action, and may even be at the root of
consciousness. Surprisingly, this idea has been explored comparatively little
in robotics. In this work, we propose a neural network architecture and
associated planning algorithm that (1) learns a representation of the world
useful for generating prospective futures after the application of high-level
actions, (2) uses this generative model to simulate the result of sequences of
high-level actions in a variety of environments, and (3) uses this same
representation to evaluate these actions and perform tree search to find a
sequence of high-level actions in a new environment. Models are trained via
imitation learning on a variety of domains, including navigation,
pick-and-place, and a surgical robotics task. Our approach allows us to
visualize intermediate motion goals and learn to plan complex activity from
visual information.
