Stochastic Methods for Neural Network Robot Control-Silicon
Valley Campus - Carnegie Mellon University  Stochastic Methods
for Neural Network Robot Control  robot movement Figure 1: Robot
learning to move in noisy environment.  Over the last two
decades significant advances have been made in the neural
network control of robot manipulators for numerous applications.
However, to date, only limited progress has been made when
considering a stochastic element in the closed loop system.
These types of stochastic models are becoming increasingly
important as the size of the physical robots continues to
shrink. We are currently pursuing an extension of existing
adaptive and neural network control algorithms to account for
various noise sources. In particular, we are analyzing the
system in a stochastic framework in which the Ito lemma of
stochastic calculus is frequently used to determine the
stochastic stability of the closed loop system. Additionally, we
have been pursuing the use of simulated annealing for neura