Biographies

Dr. Stefan Schaal
Associate Professor, Department of Computer Science and the Neuroscience Program, University of Southern California (USC)
Invited Researcher, ATR Human Information Sciences Laboratory, Japan

Dr. Stefan Schaal is an Associate Professor at the Department of Computer Science and the Neuroscience Program at the University of Southern California, and an Invited Researcher at the ATR Human Information Sciences Laboratory in Japan, where he held an appointment as Head of the Computational Learning Group during an international ERATO project, the Kawato Dynamic Brain Project (ERATO/JST).  Before joining USC, Dr. Schaal was a postdoctoral fellow at the Department of Brain and Cognitive Sciences and the Artificial Intelligence Laboratory at MIT, an Invited Researcher at the ATR Human Information Processing Research Laboratories in Japan, and an Adjunct Assistant Professor at the Georgia Institute of Technology and at the Department of Kinesiology of the Pennsylvania State University.

Dr. Schaal's Computational Learning and Motor Control Lab has its research focus in the areas of neural computation for sensorimotor control and learning.  One part of his research is concerned with learning in neural networks, statistical learning, and machine learning as the ability of learning and self-organization seems to be among the most important hallmarks of autonomous systems.  Another part of the research program focuses on how movement can be generated, in particular in human-like systems with bodies, limbs, and eyes; this research touches the fields of control theory, nonlinear control, nonlinear dynamics, optimization theory, and reinforcement learning.  In a third research branch, he investigates human performance by measuring human movement and brain activity (with fMRI) in specially designed behavioral tasks.  Such research connects closely to work in Computational Neuroscience for motor control, and it includes abstract functional models of how brains may organize sensorimotor coordination.  A fourth part of the research in lab emphasizes studies with actual humanoid robots.  Firstly, we are interested in testing our learning and control theories with real physical systems in order to evaluate the robustness of our research results.  Secondly, the humanoid robot challenges the scalability of our methods: our most advanced robot requires the nonlinear control of 30 physical degrees of freedom that need to be coordinated with visual, tactile, and acoustic perception.  When attempting to synthesize behavior with such a machine, the shortcomings of state-of-the-art learning and control theories can be discovered and addressed in subsequent research.  And thirdly, we also use the humanoid robot for direct comparisons in behavioral experiments in which the robot is treated like a regular human subject.