A Self-learning Controller For Monocular Grasping

Abstract

A method is presented to learn 3D grasping of objects with unknown dimensions using a monocular eye-in-hand manipulator. From a sequence of images a motion profile is generated to approach the object of unknown size. It is shown that monocular visual information suffices to control the deceleration of the robot manipulator. A strategy for generating learning samples is presented, and simulation results demonstrate the effectiveness of the method. I. Introduction Sensor based robot control systems can overcome many of the difficulties which are caused by unknown or uncertain models of the environment. Also, conventional sensor based control systems require explicit knowledge of the kinematics and dynamics of the robot arm and a careful calibration of the sensor system. We are interested in self-learning and adaptive systems, where an implicit model of the arm and sensor system is learned from the behaviour of the robot. Neurocomputational techniques have been successfully applied in t...