Movement Control Laboratory

Control theory that enables faster algorithms
The trouble with control optimization is that it is easier said than done. For a system with many degrees of freedom (such as a modern robot or a human body) the space of possible control strategies is vast, and finding a sensible (let alone optimal) solution automatically requires a staggering amount of computation. Computers have gotten really fast, and the multi-core revolution is great news because the necessary computations are inherently parallel. Nevertheless we need equally fast algorithms if we are to apply optimal control methodology to complex dynamical systems. Developing such algorithms as well as the underlying control theory has been a major focus of our work. This includes local trajectory-based methods [1], global function-approximation methods [2], hierarchical control methods [3], and a new framework for stochastic optimal control which makes the problem linear even though the system being controlled is non-linear [4]. We are now starting to apply our algorithms to hard control problems in robotics and biomechanics, namely legged locomotion and hand manipulation. At the same time we will continute to develop new theory and algorithms tailored to these application domains. Here are some movies illustrating the rich behaviors that can be generated fully automatically using our algorithms: arm movements, running, walking, swimming. The only thing that is designed manually here is an intuitive cost function - which prescribes spatial targets for the end-effector or center of mass, and penalizes control energy. The details of the behavior then emerge from the optimization procedure.
Department of Applied Mathematics, University of Washington, Lewis Hall #202, Box 353925, Seattle, WA 98195-3925 USA
Email 'info' (at amath.washington.edu) Phone 206-543-5493 Fax 206-685-1440