vision, computer vision Robots operating in complex dynamic working environment require the ability to ma-nipulate and grasp objects. This thesis examines previous works and the state of the art in robotic grasping and learning grasp affordances. Modern methods are surveyed, and Le’s machine learning based classifier is implemented because it provides highest suc-cess rates out of reviewed methods and is adaptable to our specific robot hardware. The implemented method uses intensity and depth features to rank grasp candidates. The performance of the implementation is presented. TIIVISTELMÄ Lappeenrannan teknillinen yliopisto