Many motor skills have an intrinsic, low-dimensional parameterization,

e.g. reaching through a grid to different targets. Repeated policy search

for new parameterizations of such a skill is inefficient, because the structure

of the skill variability is not exploited.

This issue has been previously addressed by learning mappings from task

parameters to policy parameters. In this work, we introduce a bootstrapping

technique that establishes such parameterized skills incrementally.

The approach combines iterative learning with state-of-the-art

black-box policy optimization. We investigate the benefits of

incrementally learning parameterized skills for efficient policy

retrieval and show that the number of required rollouts can be

significantly reduced when optimizing policies for novel tasks.

The approach is demonstrated for several parameterized motor

tasks including upper-body reaching motion generation for the

humanoid robot COMAN.