Title: Self-Supervised Damage-Avoiding Manipulation Strategy Optimization via Mental Simulation,
Abstract: 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, we propose to take into account any unintended
motion of objects in the scene and to learn manipulation strategies in a
self-supervised way which minimize the potential damage. The presented approach
consists of a planning method that determines the optimal sequence to
manipulate a number of objects in a scene with respect to possible damage by
simulating interaction and hence anticipating scene dynamics. The planned
manipulation sequences are taken as input to a machine learning process which
generalizes to new, unseen scenes in the same application scenario. This
learned manipulation strategy is continuously refined in a self-supervised
optimization cycle dur- ing load-free times of the system. Such a
simulation-in-the-loop setup is commonly known as mental simulation and allows
for efficient, fully automatic generation of training data as opposed to
classical supervised learning approaches. In parallel, the generated
manipulation strategies can be deployed in near-real time in an anytime
fashion. We evaluate our approach on one industrial scenario (autonomous
container unloading) and one retail scenario (autonomous shelf replenishment).