Our research focuses on skill learning in robots and learning agents in general and the development of intelligent systems that can act autonomously in uncertain and unstructured environments. Towards this goal, we study different perceptual-motor learning tasks on various robotic platforms (humanoids, manipulators, and mobile robots) in simulations and real environments and use methods from robotics and machine learning, in particular, imitation, reinforcement, and deep learning. We combine data-driven approaches with model-based formulations by exploring the mathematical structure of intelligent systems acting in a complex environment in terms of geometry, optimal control, and probability theory. Since human capabilities are far beyond those of artificial systems, we try to gain further insights into intelligent systems from studies in human motor control, biomimetics, neuroscience, and psychology.
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