  One of today's goals for industrial robot systems is to allow fast and easy
provisioning for new tasks. Skill-based systems that use planning and knowledge
representation have long been one possible answer to this. However, especially
with contact-rich robot tasks that need careful parameter settings, such
reasoning techniques can fall short if the required knowledge not adequately
modeled. We show an approach that provides a combination of task-level planning
and reasoning with targeted learning of skill parameters for a task at hand.
Starting from a task goal formulated in PDDL, the learnable parameters in the
plan are identified and an operator can choose reward functions and parameters
for the learning process. A tight integration with a knowledge framework allows
to form a prior for learning and the usage of multi-objective Bayesian
optimization eases to balance aspects such as safety and task performance that
can often affect each other. We demonstrate the efficacy and versatility of our
approach by learning skill parameters for two different contact-rich tasks and
show their successful execution on a real 7-DOF KUKA-iiwa.
