  In modern industrial settings with small batch sizes it should be easy to set
up a robot system for a new task. Strategies exist, e.g. the use of skills, but
when it comes to handling forces and torques, these systems often fall short.
We introduce an approach that provides a combination of task-level planning
with targeted learning of scenario-specific parameters for skill-based systems.
We propose the following pipeline: (1) the user provides a task goal in the
planning language PDDL, (2) a plan (i.e., a sequence of skills) is generated
and the learnable parameters of the skills are automatically identified. An
operator then chooses (3) reward functions and hyperparameters for the learning
process. Two aspects of our methodology are critical: (a) learning is tightly
integrated with a knowledge framework to support symbolic planning and to
provide priors for learning, (b) using multi-objective optimization. This can
help to balance key performance indicators (KPIs) such as safety and task
performance since they can often affect each other. We adopt a multi-objective
Bayesian optimization approach and learn entirely in simulation. We demonstrate
the efficacy and versatility of our approach by learning skill parameters for
two different contact-rich tasks. We show their successful execution on a real
7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human
robot operators.
