Combining a Monte Carlo Tree Search and a Feasibility Database to Plan and Execute Rearranging Tasks

In this paper, we address the problem of solving rearranging tasks using a robot. Rearranging tasks are challenging because they include many problems to solve at the same time, such as determining how to pick the items as well as planning how and where to place them. Solving a rearranging task usually consists of finding a set of pick-and-place instructions with a symbolic planner to perform the task. However, if the symbolic planner does not consider the robot's capability to execute the instructions, it will likely generate many infeasible instructions, which wastes time in multiple trials and failures. Therefore, we propose to combine symbolic and motion planning to confirm a sequence of instructions before its execution by the robot. To achieve this combination, we use a Motion Feasibility Checker (MFC), which selects a set of feasible poses for the robot from a feasibility database. The MFC verifies that the instructions of the symbolic planning are valid and searches for a pick-and-place pair of poses to execute the instructions. We use a Monte Carlo Tree Search (MCTS) as the symbolic planner, and we combine it with the MFC when creating or expanding the states in the tree. After the MCTS finds a set of instructions for the rearranging task, we execute those instructions with the robot. As these instructions were previously validated, the robot is able to execute them. The proposed method was tested in a simulation environment that reproduces the scenario of rearranging products on a shelf of a convenience store. The experiment results show that the proposed method outperforms the conventional method in various initial states of increasing levels of difficulty.