Teaching for multi-fingered robots based on motion intention in virtual reality

Abstract

We present teaching for multi-fingered robots based on motion intention in virtual reality. In motion intention analysis, motion is divided into plural primitive motions, those unnecessary for executing the task are deleted, and the remainder are represented by a smooth time function. Segmentation is made using 3D motion measurement of human and virtual objects and virtual reaction generated by the hand of an operator using a force-feedback glove. Analyzed motion is represented by human motion commands. A robot teaching command for the object coordinate frame is generated from a human motion command. Experimental results of a pick-and-place task verify the feasibility of proposed robot teaching.