In this paper we address the integrated prediction,
planning, and control problem that enables a single follower
robot (the "photographer") to quickly re-establish visual con-
tact with a moving target (the "subject") that has escaped the
follower’s field of view. Our work addresses this unavoidable
scenario, which reactive controllers are typically ill-equipped
to handle, by making intelligent predictions about the long-
and  short-term behavior of the target, and planning pursuit
paths that will maximize the chance of seeing the target again.
At the core of our pursuit method is the use of predictive
models of target behavior, which help narrow down the possible
future locations of the target to a few discrete hypotheses, as
well as the use of combinatorial search in physical space to
check those hypotheses efficiently. We model target behavior in
terms of a learned navigation reward function, using Inverse
Reinforcement Learning, based on semantic terrain features
of satellite maps. Our pursuit algorithm continuously predicts
the latent destination of the target, and its position in the
future, and relies on efficient graph representation and search
methods in order to navigate to location hypotheses at which
the target is most likely to be seen at an anticipated time.
We perform extensive evaluation of our predictive pursuit
algorithm over multiple satellite maps, under thousands of
simulation scenarios, against state-of-the art MDP and POMDP
solvers, and we show that our method significantly outperforms
them by exploiting domain-specific knowledge, while being able
to run in real-time.