Adaptive Control for Human-Robot Skilltransfer: Trajectory Planning Based on Fluid Dynamics

Abstract

A popular method for an easy and also flexible programming of robots is learning by demonstration. An intelligent controller learns a task from several examples carried out by an experienced user. Afterwards, the task can be adapted to new, formerly unknown environments. One particular challenge arising with this technique is generalization of demonstrations in order to get a generic description of the task. In this paper a new methodology for solving this problem is proposed. The main part of the algorithm exploits principles known from fluid dynamics.