  Prospection is an important part of how humans come up with new task plans,
but has not been explored in depth in robotics. Predicting multiple task-level
is a challenging problem that involves capturing both task semantics and
continuous variability over the state of the world. Ideally, we would combine
the ability of machine learning to leverage big data for learning the semantics
of a task, while using techniques from task planning to reliably generalize to
new environment. In this work, we propose a method for learning a model
encoding just such a representation for task planning. We learn a neural net
that encodes the $k$ most likely outcomes from high level actions from a given
world. Our approach creates comprehensible task plans that allow us to predict
changes to the environment many time steps into the future. We demonstrate this
approach via application to a stacking task in a cluttered environment, where
the robot must select between different colored blocks while avoiding
obstacles, in order to perform a task. We also show results on a simple
navigation task. Our algorithm generates realistic image and pose predictions
at multiple points in a given task.
