Robot imitation of human motion based on qualitative description from multiple measurement of human and environmental data

Abstract

This paper proposes an imitation algorithm for a robot to acquire typical tasks from multiple measured data of human tasks in the daily life. The algorithm consists of the following procedures: 1) Firstly, the system measures multiple human's object-transferring tasks on a table. Then it calculates qualitative description from measured raw data of positions of human hand and object, as well as the force applied to the table. This description is then converted to probabilistic description. 2) Secondly, the system finds typical human tasks with the maximum likelihood from the probabilistic description. 3) Thirdly, the trajectory, which enables the robot to imitate the typical human task, is extracted. 4) Finally, the imitation task within limited force to the environment is generated from the trajectory by simulation and adaptation. The experimental execution of the generated trajectory proves the validity of the algorithm.