AIC Seminar Series
Learning to Swim, Walk, and Grasp using Shaped Manifold Control
|Bill Smart||Washington University in St. Louis||[Home Page]|
Notice: hosted by Brian Gerkey
Date: 2007-10-11 at 16:00
Location: EJ291 (SRI E building) (Directions)
Learning to control high-dimensional, non-linear dynamical systems is
hard, in part because of the Curse of Dimensionality. The volume of the
state space increases exponentially with the number of state variables
used to describe the system. Learning a controller over this space
often requires an exponential amount of training data, limiting us to
relatively low-dimensional systems.
Many robotic systems, however, do not inhabit the entire volume of the
state space. In fact, any system with a periodic gait lives on a
1-dimensional manifold embedded in the full state space of the system.
In this talk, we introduce Shaped Manifold Control, which simultaneously
estimates the manifold over which the system operates and learns an
effective controller over this manifold. SMC sidesteps the curse of
dimensionality because is learns over a 1-dimensional manifold,
regardless of the dimensionality of the full state space. We have
successfully applied SMC to a number of simulated high-dimensional
continuous dynamical systems, including swimming and walking robots, and
will also discuss our plans for using it for the control of a robotic
hand prosthesis using direct cortical control.