  Non-monotone object rearrangement planning in confined spaces such as
cabinets and shelves is a widely occurring but challenging problem in robotics.
Both the robot motion and the available regions for object relocation are
highly constrained because of the limited space. This work proposes a
Multi-Stage Monte Carlo Tree Search (MS-MCTS) method to solve non-monotone
object rearrangement planning problems in confined spaces. Our approach
decouples the complex problem into simpler subproblems using an object stage
topology. A subgoal-focused tree expansion algorithm that jointly considers the
high-level planning and the low-level robot motion is designed to reduce the
search space and better guide the search process. By fitting the task into the
MCTS paradigm, our method produces optimistic solutions by balancing
exploration and exploitation. The experiments demonstrate that our method
outperforms the existing methods in terms of the planning time, the number of
steps, and the total move distance. Moreover, we deploy our MS-MCTS to a
real-world robot system and verify its performance in different scenarios.
