Nonprehensile multiobject rearrangement is the robotic task of planning feasible paths and
transferring multiple objects to their predefined target poses without grasping. It must
consider how each object reaches the target and the order in which objects move,
considerably increasing the complexity of the problem. Thus, we propose a hierarchical
policy for nonprehensile multiobject rearrangement based on deep-reinforcement learning.
We use imitation learning and reinforcement learning to train a rollout policy. In a high-level …