  Planning in realistic environments requires searching in large planning
spaces. Affordances are a powerful concept to simplify this search, because
they model what actions can be successful in a given situation. However, the
classical notion of affordance is not suitable for long horizon planning
because it only informs the robot about the immediate outcome of actions
instead of what actions are best for achieving a long-term goal. In this paper,
we introduce a new affordance representation that enables the robot to reason
about the long-term effects of actions through modeling what actions are
afforded in the future, thereby informing the robot the best actions to take
next to achieve a task goal. Based on the new representation, we develop a
learning-to-plan method, Deep Affordance Foresight (DAF), that learns partial
environment models of affordances of parameterized motor skills through
trial-and-error. We evaluate DAF on two challenging manipulation domains and
show that it can effectively learn to carry out multi-step tasks, share learned
affordance representations among different tasks, and learn to plan with
high-dimensional image inputs. Additional material is available at
https://sites.google.com/stanford.edu/daf
