  Everyday robotics are challenged to deal with autonomous product handling in
applications like logistics or retail, possibly causing damage on the items
during manipulation. Traditionally, most approaches try to minimize physical
interaction with goods. However, this paper proposes to take into account any
unintended object motion and to learn damage-minimizing manipulation strategies
in a self-supervised way. The presented approach consists of a simulation-based
planning method for an optimal manipulation sequence with respect to possible
damage. The planned manipulation sequences are generalized to new, unseen
scenes in the same application scenario using machine learning. This learned
manipulation strategy is continuously refined in a self-supervised,
simulation-in-the-loop optimization cycle during load-free times of the system,
commonly known as mental simulation. In parallel, the generated manipulation
strategies can be deployed in near-real time in an anytime fashion. The
approach is validated on an industrial container-unloading scenario and on a
retail shelf-replenishment scenario.
