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 ,
global function-approximation methods ,
hierarchical control methods , and a new framework for
stochastic optimal control which makes the problem linear even though the system being controlled
is non-linear . 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:
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.