Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search

Understand

Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping.

• It needs to consider how each object reaches the target and the order of object movement, which significantly deepens the complexity of the problem.
• To address these challenges, we propose a hierarchical policy to divide and conquer for non-prehensile multi-object rearrangement.
• In the high-level policy, guided by a designed policy network, the Monte Carlo Tree Search efficiently searches for the optimal rearrangement sequence among multiple objects, which benefits from imitation and reinforcement.