I am interested in autonomous learning, that is how an embodied agent can determine what to learn, how to learn, and how to judge the learning success. I believe, that robots need to learn from experience to become dexterous and versatile assistants to humans in many real-world domains. Intrinsically motivated learning can help to create a suitable learning curriculum and lead to capable systems without the need to specify every little detail of that process. Here we take inspiration from child development. 

In my research group, we are investigating and developing machine learning methods for efficient and self-motivated learning in robots. To achieve this, we are looking into model-based reinforcement learning and internal model learning, formulations of artificial intrinsic motivation, and information theory (Reinforcement learning and Control). For obtaining more capable agents, we are studying the integration of combinatorial algorithms into deep neural networks (Deep Learning). Along the way, we are making contributions to general machine learning and deep learning in particular (Deep Learning). 
Another thrust of research is the development of haptic sensors for robots, as this sensing modality his so far underdeveloped but highly relevant for self-learning robots (Robotics and Sensation). A longer research overview can be found here.

Google scholar: scholar.google.de/citations?user=b-JF-UIAAAAJ

List of publication: see publication tab 
Old websites with some videos from playful machines: playfulmachines.com and robot.informatik.uni-leipzig.de

Before joining the MPI in Tübingen, I was postdoc fellow at the IST Austria in the groups of Christoph Lampert and Gašper Tkačik after being a postdoc at the Max Planck Institute for Mathematics in the Sciences in Leipzig. I received my PhD in 2009 in Göttingen (Germany), and studied computer science in Leipzig (Germany) and Edinburgh (UK). 

autonomous learning machine learning robotics behavior intrinsic motivation self-organization self-exploration