  One effective approach for equipping artificial agents with sensorimotor
skills is to use self-exploration. To do this efficiently is critical, as time
and data collection are costly. In this study, we propose an exploration
mechanism that blends action, object, and action outcome representations into a
latent space, where local regions are formed to host forward model learning.
The agent uses intrinsic motivation to select the forward model with the
highest learning progress to adopt at a given exploration step. This parallels
how infants learn, as high learning progress indicates that the learning
problem is neither too easy nor too difficult in the selected region. The
proposed approach is validated with a simulated robot in a table-top
environment. The simulation scene comprises a robot and various objects, where
the robot interacts with one of them each time using a set of parameterized
actions and learns the outcomes of these interactions. With the proposed
approach, the robot organizes its curriculum of learning as in existing
intrinsic motivation approaches and outperforms them in learning speed.
Moreover, the learning regime demonstrates features that partially match infant
development; in particular, the proposed system learns to predict the outcomes
of different skills in a staged manner.
