Hiroaki Gomi, Mitsuo Kawato
Recognition of Manipulated Objects
by Motor Learning
with Modular Architecture Networks
Abstract:For recognition and control of multiple manipulated objects, we present two
learning schemes for neural-network controllers based on feedback-error-learning
and modular architecture. In both schemes, the network consists of
a recognition network and modular control networks. In the first scheme, a
Gating Network is trained to acquire object-specific representations for
recognition of a number of objects (or sets of objects). In the second
scheme, an Estimation Network is trained to acquire function-specific, rather
than object-specific, representations which directly estimate physical
parameters. Both recognition networks are trained to identify manipulated
objects using somatic and/or visual information. After learning, appropriate
motor commands for manipulation of each object are issued by the control
networks which have a modular structure. By simulation of simple examples,
the potential advantages and disadvantages of the two schemes are examined.