Task-Difficulty-Aware Object Arrangement by Taking Advantage of Tossing Motions

Abstract

This study aims to realize a pick-and-toss operation that can replace pick-and-place, thereby allowing the robot to expand its work range and perform tasks more efficiently. While pick-and-toss improves the speed of object arrangement tasks, the placement environment influences the success or failure of the tossing operation. Therefore, to achieve quick and accurate object arrangement tasks, we propose to determine the preferred operation from pick-and-place and pick-and-toss tasks by considering the task difficulty estimated from the target placement environment. The proposed method can simultaneously acquire the tossing trajectory through self-supervised learning and the task decision policy through a blue force search. Our experimental results demonstrated the effectiveness of the proposed method through simulations and real-world experiments of arrangement tasks for several different rectangular shapes.