  Intrinsically motivated goal exploration algorithms enable machines to
discover repertoires of policies that produce a diversity of effects in complex
environments. These exploration algorithms have been shown to allow real world
robots to acquire skills such as tool use in high-dimensional continuous state
and action spaces. However, they have so far assumed that self-generated goals
are sampled in a specifically engineered feature space, limiting their
autonomy. In this work, we propose to use deep representation learning
algorithms to learn an adequate goal space. This is a developmental 2-stage
approach: first, in a perceptual learning stage, deep learning algorithms use
passive raw sensor observations of world changes to learn a corresponding
latent space; then goal exploration happens in a second stage by sampling goals
in this latent space. We present experiments where a simulated robot arm
interacts with an object, and we show that exploration algorithms using such
learned representations can match the performance obtained using engineered
representations.
