Rearranging Tasks in Daily-life Environments Using a Monte Carlo Tree Search and a Feasibility Database

Abstract

In this paper, we address the task of rearranging items with a robot. A rearranging task is challenging because one should solve the following issues: to determine how to pick the items and plan how and where to place the items. In this study, we focus on how to obtain a sequence of actions that the robot could execute reducing the failures when the motion planner creates the trajectory to move the robot, such as not finding a solution. To confirm the sequence of instructions before executing them with the robot, we combine a motion planner with a symbolic planner. For that purpose, we propose a Motion Feasibility Checker (MFC), which quickly decides if a given set of pick-and-place poses can be executed with respect to the robot's kinematics. The MFC uses a database of possible pick and place poses of the target robot; given the initial and target pose of the item, the MFC finds a set of pick-and-place poses to execute that action with the robot. We use the Monte Carlo Tree Search (MCTS) to achieve a high performance of the symbolic planning. In the proposed method, the MCTS searches for the goal while it collaborates with the MFC. We tested the proposed method in a simulation environment doing a sandwich rearranging task in a convenience store setup.