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Stefan Schaal

Associate Professor, Computer Science and Neuroscience, & ATR Computational Neuroscience Laboratory, Kyoto, Japan.

Stefan  Schaal

Research Topics

  • Statistical learning
  • motor control
  • computational neuroscience
  • biomimetic and humanoid robotics
  • nonlinear dynamics
  • motor psychophysics
  • neuroimaging

Research Images

Differences between point-to-point (discrete) and oscillatory (rhythmic) wrist movement as revealed by an fMRI experiment.A prototype of the humanoid robot that will be used in our research at USC and international collaborations.This exoskelton motion capture system allows monitoring 35 degrees-of-freedom of the human body simultaneously.

Research Overview

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 focusses 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 design 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 forth 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 (similar to the picture above) 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.

Contact Information

Mailing Address University of Southern California
3614 Watt Way, HNB 103
Los Angeles, CA 90089-2520
Office Location RTH 401
Office Phone (213) 740-9418
Lab Location RTH 417/416A
Lab Phone (213) 740-6717
Fax (213) 740-1510
Office Location RTH 401



  • M.S., Technical University of Munich, 1988.
  • Ph.D., Technical University of Munich, 1991.
  • Post-Doctoral Fellow, MIT, 1991-1994.

Selected Publications

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  • Ting JA, D'Souza A, Yamamoto K, Yoshioka T, Hoffman D, Kakei S, Sergio L, Kalaska J, Kawato M, Strick P, Schaal S. (2008) Variational Bayesian least squares: An application to brain-machine interface data. Neural Netw. (In press). PubMed
  • Schaal S, Nakamura Y, Dario P. (2008) Special issue on robotics and neuroscience. Neural Netw. 21(4):551-552. PubMed
  • Peters J, Schaal S. (2008) Reinforcement learning of motor skills with policy gradients. Neural Netw. 21(4):682-697. PubMed
  • Schaal S, Mohajerian P, Ijspeert A. (2007) Dynamics systems vs. optimal control - a unifying view. Prog Brain Res. 165:425-445. PubMed
  • Billard A, Schaal S. (2006) Special issue on the brain mechanisms of imitation learning. Neural Netw. 19(3):251-253. PubMed
  • Schaal S, Elbert T. (2006) Ten years after the genocide: trauma confrontation and posttraumatic stress in Rwandan adolescents. J Trauma Stress. 19(1):95-105. PubMed
  • Schaal S, Schweighofer N. (2005) Computational motor control in humans and robots. Curr Opin Neurobiol. 15(6):675-82. PubMed