Trajectory Generation of A Human-Friendly Robot using An Interactive Genetic Algorithm

Abstract

This paper deals with human-friendly trajectory generation of a robot manipulator. Various methods for the trajectory generation have been proposed so far, but robots must deal with environments including human operators. This paper applies an interactive genetic algorithm for the trajectory generation using human evaluation, but the detail of the human evaluation is not clear. Therefore, we must estimate human evaluation through the optimization process, and we use a state-value function used often in reinforcement learning. Furthermore, we discuss the effectiveness of the proposed method through some experiments of the robot manipulator