  Identifying internal parameters for planning is crucial to maximizing the
performance of a planner. However, automatically tuning internal parameters
which are conditioned on the problem instance is especially challenging. A
recent line of work focuses on learning planning parameter generators, but lack
a consistent problem definition and software framework. This work proposes the
unified planner optimization problem (POP) formulation, along with the Open
Planner Optimization Framework (OPOF), a highly extensible software framework
to specify and to solve these problems in a reusable manner.
