  We present a framework for learning useful subgoals that support efficient
long-term planning to achieve novel goals. At the core of our framework is a
collection of rational subgoals (RSGs), which are essentially binary
classifiers over the environmental states. RSGs can be learned from
weakly-annotated data, in the form of unsegmented demonstration trajectories,
paired with abstract task descriptions, which are composed of terms initially
unknown to the agent (e.g., collect-wood then craft-boat then go-across-river).
Our framework also discovers dependencies between RSGs, e.g., the task
collect-wood is a helpful subgoal for the task craft-boat. Given a goal
description, the learned subgoals and the derived dependencies facilitate
off-the-shelf planning algorithms, such as A* and RRT, by setting helpful
subgoals as waypoints to the planner, which significantly improves
performance-time efficiency.
