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.

翻译：识别规划过程中的内部参数对于最大化规划器性能至关重要。然而，自动调整依赖于问题实例的内部参数尤为困难。近期一系列研究聚焦于学习规划参数生成器，但缺乏统一的问题定义和软件框架。本文提出了统一的规划器优化问题（POP）形式化定义，以及高度可扩展的开放规划器优化框架（OPOF），能够以可复用的方式规范并解决此类问题。