Virtual Robot Teaching Based on Motion Analysis and Hand Manipulability for Multi-Fingered Robot

Abstract

A virtual robot teaching system, consisting of human demonstration and motion-intention analysis in a virtual reality environment, is an advanced form of automatic programming for multi-fingered robots. We propose a new segmentation method to analyze human motion data and a virtual teaching system based on hand manipulability, in which the position and orientation of the robot hand are determined so as to maximize the robot hand's manipulability. In the segmentation method, human motion data consisting of contact points, grasp force, hand and object position, and the like are segmented into plural primitive motions and the type of task is analyzed based on the sequence of the primitive motions. A trial assembly task using a humanoid robot hand named Gifu Hand III is shown to demonstrate the effectiveness of the proposed method.