  In this paper we present a neurally plausible model of robot reaching
inspired by human infant reaching that is based on embodied artificial
intelligence, which emphasizes the importance of the sensory-motor interaction
of an agent and the world. This model encompasses both learning sensory-motor
correlations through motor babbling and also arm motion planning using
spreading activation. This model is organized in three layers of neural maps
with parallel structures representing the same sensory-motor space. The motor
babbling period shapes the structure of the three neural maps as well as the
connections within and between them. We describe an implementation of this
model and an investigation of this implementation using a simple reaching task
on a humanoid robot. The robot has learned successfully to plan reaching
motions from a test set with high accuracy and smoothness.
