Russ Tedrake

Position: Assistant Professor (EECS)

Office: 32-232

Phone:253-1778

E-mail: russt@csail.mit.edu

Research Directorate(s): AI

URL: http://people.csail.mit.edu/russt/

Biography:

Russ Tedrake is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT, and a member of the Computer Science and Artificial Intelligence Lab. He received his B.S.E. in Computer Engineering from the University of Michigan, Ann Arbor, in 1999, and his Ph.D. in Electrical Engineering and Computer Science from MIT in 2004, working with Sebastian Seung. After graduation, he spent a year with the MIT Brain and Cognitive Sciences Department as a Postdoctoral Associate. During his education, he has spent time at Microsoft, Microsoft Research, and the Santa Fe Institute.

Professor Tedrake’s research group is interested in underactuated motor control systems in animals and machines that are capable of executing dynamically dexterous tasks and interacting with uncertain environments. They believe that the design of these control systems is intimately related to the mechanical designs of their machines, and that tools from machine learning and optimal control can be used to exploit this coupling when classical control techniques fail. Current projects include robust and efficient bipedal locomotion on flat terrain, multi-legged locomotion over extreme terrain, flapping-winged flight, and feedback control for fluid dynamics.

Recent and/or Significant Publications:

Steven H. Collins, Andy Ruina, Russ Tedrake, and Martijn Wisse. (2005) Efficient bipedal robots based on passive-dynamic walkers. Science, 307:1082-1085, February 18 2005.

Jerry E. Pratt and Russ Tedrake. (2005) Velocity-based Stability Margins for Fast Bipedal Walking. In Proceedings of the First Ruperto Carola Symposium on Fast Motions in Biomechanics and Robotics: Optimization and Feedback Control. Heidelberg, Germany, September 2005.

Russ Tedrake, Teresa Weirui Zhang, and H. Sebastian Seung. (2004) Stochastic Policy Gradient Reinforcement Learning on a Simple 3D Biped. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pages 2849-2854, Senda, Japan, September 2004.