Apparatus for and method of simulating robot arm motion planning, to which curriculum-based deep reinforcement learning applies

Abstract

A method of simulating robot arm motion planning to which curriculum-based deep reinforcement learning applies may be provided. The method according to an embodiment of the present disclosure may include: generating multiple features for each target point within a robot arm's work area; deciding on a selected feature based on correlations between the multiple features; determining a level of difficulty of the each target point based on the selected feature; determining a unit of curriculum (UoC), which is a group of the targets points, for which deep reinforcement learning is to be performed in the same episode, based on the determined level of difficulty; and performing sequentially the deep reinforcement learning for each of the units of curriculum.