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Non-monotone object rearrangement planning in confined spaces such ascabinets and shelves is a widely occurring but challenging problem in robotics.Both the robot motion and the available regions for object relocation arehighly constrained because of the limited space. This work proposes aMulti-Stage Monte Carlo Tree Search (MS-MCTS) method to solve non-monotoneobject rearrangement planning problems in confined spaces. Our approachdecouples the complex problem into simpler subproblems using an object stagetopology. A subgoal-focused tree expansion algorithm that jointly considers thehigh-level planning and the low-level robot motion is designed to reduce thesearch space and better guide the search process. By fitting the task into theMCTS paradigm, our method produces optimistic solutions by balancingexploration and exploitation. The experiments demonstrate that our methodoutperforms the existing methods in terms of the planning time, the number ofsteps, and the total move distance. Moreover, we deploy our MS-MCTS to areal-world robot system and verify its performance in different scenarios.

Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments