Machine learning of motor skills for robotics

Autonomous robots have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches in the 1980s showed that approaches purely based on reasoning and human insights would not be applicable to all perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning. However, to date, learning techniques have yet to fulfill this promise as only few task-independent methods scale into manipulator or humanoid robotics. In order to overcome these difficulties, we investigate steps towards a structured, general approach to motor skill learning based on a theoretically well-founded novel framework for task control and appropriate learning algorithms.