The focus of our research is 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 in 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 for the development of intelligent systems from studies in human motor control, biomimetics, neuroscience and psychology.

Current Research Topics:

  • Learning agents and intelligent robots
  • Deep reinforcement learning
  • Imitation learning
  • Mathematics for intelligent systems