Interactive Trajectory Generation for A Partner Robot Based on Modular Neural Networks

Abstract

This paper proposes modular neural networks for trajectory learning and a steady-state algorithm for trajectory generation used in the imitation of a partner robot interaction with a human. Various type of genetic algorithm have been applied for trajectory generation of robot manipulators. In this paper, we propose a trajectory motions pattern, and compare the proposed method with its related methods. Finally, we show experimental results of trajectory generation through interaction with human.