Object Rearrangement Planning for Target Retrieval in a Confined Space with Lateral View

Abstract

In this paper, we perform an object rearrangement task for target retrieval in an environment with a confined space and limited observation directions. The agent must create a collision-free path to bring out the target object by relocating the surrounding objects using the prehensile action, i.e., pick-and-place. Object rearrangement in a confined space is a non-monotone problem, and finding a valid plan within a reasonable time is challenging. We propose a novel algorithm that divides the target retrieval task, which requires a long sequence of actions, into sequential sub-problems and explores each solution through Monte Carlo tree search (MCTS). In the experiment, we verify that the proposed algorithm can find safe rearrangement plans with various objects efficiently compared to the existing planning methods. Furthermore, we show that the proposed method can be transferred to a real robot experiment without additional training.