I have a wide range of research interests, which are all related to better understand the mechanisms underlying intelligent behavior and how the resulting knowledge gain can be transformed into technological innovation. In particular, my research focuses on novel strategies for adaptation in behavior and morphology of cognitive robots. My long-term vision is to build cognitive artifacts (such as real robots and self-managing computing systems) which can adapt at run-time to changing service and performance requirements without the need for external re-programming, re-configuring, or re-adjusting. I approach all relevant research issues through a combination of robotics, computer modeling, complex systems science, computational biology, statistical physics, as well as data-driven and model-driven probabilistic techniques. I am convinced that such interdisciplinarity provides an important key to gain insights into otherwise puzzling properties of intelligent systems while suggesting biologically-inspired solutions to fundamental problems in robotics and artificial intelligence.

1. Lungarella, M., Iida, F., Bongard, J.C. and Pfeifer, R. (2008). Essays Dedicated to the 50th Anniversary of Artificial Intelligence, Lecture Notes on Artificial Intelligence, Berlin: Springer-Verlag.
2. Pfeifer, R., Lungarella, M., Iida, F. (2007). Self-organization, embodiment, and biologically inspired robotics. Science, 318:1088-1093.
3. Lungarella, M., Pitti, A., and Kuniyoshi, Y. (2007). Information transfer at multiple scales. Physical Review E, 056117.
4. Lungarella, M., Ishiguro, K., Kuniyoshi, Y. and Otsu, N. (2007). Methods for quantifying the causal structure of bivariate time series. Int. J. of Bifurcation and Chaos, 17(3):903-921.
5. Lungarella, M. (2007). Developmental robotics. Scholarpedia, 2(8):3104.
6. Lungarella, M. and Sporns, O. (2006). Mapping information flow in sensorimotor networks. PLoS Computational Biology, 2(10):e144.