Object manipulation by learning stereo vision-based robots

Abstract

An approach to realize learning calibration-free stereo vision-based robot for manipulating objects is introduced. It allows a robot gather experiences through interaction with the world and continuously improve its performance based on the collected experiences. It uses a direct transition from image coordinates to motor control commands, but no world coordinates and no inverse perspective or kinematic transformations. The approach has been tested in real-world experiences on an uncalibrated vision-guided manipulator with five degrees of freedom to grasp a variety of differently shaped objects.