  We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive
Curiosity (SAGG-RIAC) architecture as an intrinsi- cally motivated goal
exploration mechanism which allows active learning of inverse models in
high-dimensional redundant robots. This allows a robot to efficiently and
actively learn distributions of parameterized motor skills/policies that solve
a corresponding distribution of parameterized tasks/goals. The architecture
makes the robot sample actively novel parameterized tasks in the task space,
based on a measure of competence progress, each of which triggers low-level
goal-directed learning of the motor policy pa- rameters that allow to solve it.
For both learning and generalization, the system leverages regression
techniques which allow to infer the motor policy parameters corresponding to a
given novel parameterized task, and based on the previously learnt
correspondences between policy and task parameters. We present experiments with
high-dimensional continuous sensorimotor spaces in three different robotic
setups: 1) learning the inverse kinematics in a highly-redundant robotic arm,
2) learning omnidirectional locomotion with motor primitives in a quadruped
robot, 3) an arm learning to control a fishing rod with a flexible wire. We
show that 1) exploration in the task space can be a lot faster than exploration
in the actuator space for learning inverse models in redundant robots; 2)
selecting goals maximizing competence progress creates developmental
trajectories driving the robot to progressively focus on tasks of increasing
complexity and is statistically significantly more efficient than selecting
tasks randomly, as well as more efficient than different standard active motor
babbling methods; 3) this architecture allows the robot to actively discover
which parts of its task space it can learn to reach and which part it cannot.
