Neural Network-based Motion Feasibility Checker to Validate Instructions in Rearrangement Tasks before Execution by Robots

In this paper, we address the task of rearranging items with a robot. A rearrangement task is challenging because it requires us to solve the following issues: determine how to pick the items and plan how and where to place the items. In our previous work, we proposed to solve a rearrangement task by combining the symbolic and motion planners using a Motion Feasibility Checker (MFC) and a Monte Carlo Tree Search (MCTS). The MCTS searches for the goal while it collaborates with the MFC to accept or reject instructions. We could solve the rearrangement task, but one drawback is the time it takes to find a solution. In this study, we focus on quickly accepting or rejecting tentative instructions obtained from an MCTS. We propose using a Neural Network-based Motion Feasibility Checker (NN-MFC), a Fully Connected Neural Network trained with data obtained from the MFC. This NN-MFC quickly decides if the instruction is valid or not, reducing the time the MCTS uses to find a solution to the task. The NN-MFC determines the validity of the instruction based on the initial and target poses of the item. Before the final execution of the instructions, we re-validate the instructions with the MFC as a confirmation before execution. We tested the proposed method in a simulation environment by doing an item rearrangement task in a convenience store setup.