Hierarchical policy with deep-reinforcement learning for nonprehensile multiobject rearrangement

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 policy, the policy network directs the Monte Carlo tree search algorithm to efficiently seek the ideal rearrangement sequence for several items. In a low-level policy, the robot plans the paths according to the order of path primitives and manipulates the objects to approach the target poses one by one. Our experiments show that the proposed method has a higher success rate, fewer steps, and shorter path length than the state-of-the-art methods.