This seminar will be held in the TSRB Banquet Hall from 12-1 p.m. and is open to the public.
MIT's Russ Tedrake presents "Algebraic Methods for Nonlinear Dynamics and Control" as part of the Robotics Seminar Series.
Some years ago, experiments with passive dynamic walking convinced me that finding efficient algorithms to reason about the nonlinear dynamics of our machines would be the key to turning a lumbering humanoid into a graceful ballerina. For linear systems (and nearly linear systems), these algorithms already exist—many problems of interest for design and analysis can be solved very efficiently using convex optimization. In this talk, I'll describe a set of relatively recent advances using polynomial optimization that are enabling a similar convex-optimization-based approach to nonlinear systems. I will give an overview of the theory and algorithms, and demonstrate their application to hard control problems in robotics, including dynamic legged locomotion, humanoids and robotic birds. Surprisingly, this polynomial (aka algebraic) view of rigid body dynamics also extends naturally to systems with frictional contact—a problem which intuitively feels very discontinuous.
Russ Tedrake is an associate 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 BSE in computer engineering from the University of Michigan, Ann Arbor, in 1999, and his PhD in electrical engineering and computer science from MIT in 2004, working with Sebastian Seung. After graduation, Tedrake spent a year with the MIT Brain and Cognitive Sciences Department as a postdoctoral associate. He has also spent time at Microsoft, Microsoft Research, and the Santa Fe Institute.
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. The design of these control systems is intimately related to the mechanical designs of machines and tools from machine learning and optimal control can be used to exploit this coupling when classical control techniques fail. Current research projects in Tedrake's group include robust and efficient bipedal locomotion on flat terrain, multi-legged locomotion over extreme terrain, flapping-winged flight, and feedback control for fluid dynamics.