Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by parallelizing state expansions, whereas another algorithm ePA*SE achieves this by effectively parallelizing edge evaluations. ePA*SE targets domains in which the action space comprises actions with expensive but similar evaluation times. However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome. Motivated by this, we introduce GePA*SE: Generalized Edge-based Parallel A* for Slow Evaluations, which generalizes the key ideas of PA*SE and ePA*SE i.e. parallelization of state expansions and edge evaluations respectively. This extends its applicability to domains that have actions requiring varying computational effort to evaluate them. In particular, we focus on manipulation planning for a high-DoF robot arm which has an action space comprising both cheap and expensive to compute motion primitives. The open-source code for GePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search

提出了一种名为GePA * SE的算法，它是PA * SE和ePA * SE的泛化，可以在具有评估时间变化区间的任务上实现并行搜索，可用于机器人领域。

GePA*SE: 用于缓慢评估的广义基于边缘的并行A*算法

GePASE: 用于缓慢评估的广义基于边缘的并行A算法