Self-Supervised Damage-Avoiding Manipulation Strategy Optimization via Mental Simulation
This addresses damage avoidance in robotics for logistics and retail, but it appears incremental as it builds on simulation-based planning and self-supervised learning.
The paper tackles the problem of autonomous product handling in robotics, where unintended object motion can cause damage, by proposing a self-supervised method to learn damage-minimizing manipulation strategies through mental simulation, validated in industrial and retail scenarios.
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.