  This paper presents a technical approach to robot learning of motor skills
which combines active intrinsically motivated learning with imitation learning.
Our architecture, called SGIM-D, allows efficient learning of high-dimensional
continuous sensorimotor inverse models in robots, and in particular learns
distributions of parameterised motor policies that solve a corresponding
distribution of parameterised goals/tasks. This is made possible by the
technical integration of imitation learning techniques within an algorithm for
learning inverse models that relies on active goal babbling. After reviewing
social learning and intrinsic motivation approaches to action learning, we
describe the general framework of our algorithm, before detailing its
architecture. In an experiment where a robot arm has to learn to use a flexible
fishing line , we illustrate that SGIM-D efficiently combines the advantages of
social learning and intrinsic motivation and benefits from human demonstration
properties to learn how to produce varied outcomes in the environment, while
developing more precise control policies in large spaces.
