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

Hanwen Ren, Ahmed H Qureshi

Abstract:

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 lowlevel 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
regarding the planning time, the number of steps, and the total
move distance. Moreover, we deploy our MS-MCTS to a realworld robot system and verify its performance in different
confined environments.