Implementing machine learning in Industrial Robots for better human-robot cooperation

Industrial robotic control has not changed significantly over the last years. Robots still rely on pre- determined machine instructions in order to work. Newly developed cooperative robots uses the same control mechanisms. This method has little input from the environment to make decisions. This obstructs human-robotic cooperation during operation. In order to improve human-robotic cooperation, a design of a new controller for industrial robots is presented enabling a more human-friendly cooperation trough machine learning algorithms. These algorithms create other methods to program industrial robots, and makes robots more exible. A prototype is build as a proof of concept. It also forms a base which enables future development of software for the ABB YuMi robot.