  The act of reaching for an object is a fundamental yet complex skill for a
robotic agent, requiring a high degree of visuomotor control and coordination.
In consideration of dynamic environments, a robot capable of autonomously
adapting to novel situations is desired. In this paper, a developmental
robotics approach is used to autonomously learn visuomotor coordination on the
NICO (Neuro-Inspired COmpanion) platform, for the task of object reaching. The
robot interacts with its environment and learns associations between motor
commands and temporally correlated sensory perceptions based on Hebbian
learning. Multiple Grow-When-Required (GWR) networks are used to learn
increasingly more complex motoric behaviors, by first learning how to direct
the gaze towards a visual stimulus, followed by learning motor control of the
arm, and finally learning how to reach for an object using eye-hand
coordination. We demonstrate that the model is able to deal with an unforeseen
mechanical change in the NICO's body, showing the adaptability of the proposed
approach. In evaluations of our approach, we show that the humanoid robot NICO
is able to reach objects with a 76% success rate.
