Learning how to approach industrial robot tasks from natural demonstrations

In the last years, Robot Learning from Demonstration (RLfD) [1] [2] has become a major topic in robotics research. The main reason for this is that programming a robot can be a very difficult and time spending task. The RLfD paradigm has been applied to a great variety of robots, but it is still difficult to make the robot learn a task properly. Often the teacher is not an expert in the field, and viceversa an expert could not know well enough the robot to be a teacher. With this paper, we aimed at closing this gap by proposing a novel motion re-targeting technique to make a manipulator learn from natural demonstrations. A RLfD framework based on Gaussian Mixture Models (GMM) and Gaussian Mixture Regressions (GMR) was set to test the accuracy of the system in terms of precision and repeatability. The robot used during the experiments is a Comau Smart5 SiX and a novel virtual model of this manipulator has also been developed to simulate an industrial scenario which allows valid experimentation while avoiding damages to the real robot.