My research focusses on a better understanding of motor skill learning and execution in both man and machines. To accomplish this goal, I am interested both into endowing robots with new skills as well as to improve our understanding of human motor skills through computational models of learning and control. Thus, I am using methods from artificial intelligence to synthesize new machine learning algorithms and motor learning architectures to enable anthropomorphic robots to acquire new skills such as juggling, table tennis, grasping & manipulation, locomotion and many more. Similarly, I am interested at using similar machine learning for analysis of behavior from a cognitive science point of view to improve our understanding of human motor abailities, for example in human ball catching, human tactile manipulation or human table tennis. My long-term goal is to provide a unified framework for understanding motor control that enables us to take our robots out of the research and factory floors into human-inhabited environments.