  Robots need object-level scene understanding to manipulate objects while
reasoning about contact, support, and occlusion among objects. Given a pile of
objects, object recognition and reconstruction can identify the boundary of
object instances, giving important cues as to how the objects form and support
the pile. In this work, we present a system, SafePicking, that integrates
object-level mapping and learning-based motion planning to generate a motion
that safely extracts occluded target objects from a pile. Planning is done by
learning a deep Q-network that receives observations of predicted poses and a
depth-based heightmap to output a motion trajectory, trained to maximize a
safety metric reward. Our results show that the observation fusion of poses and
depth-sensing gives both better performance and robustness to the model. We
evaluate our methods using the YCB objects in both simulation and the real
world, achieving safe object extraction from piles.
