Automated task planning using object arrangement optimization

We present a method enabling a robot to automatically arrange objects using task and motion planning. Given an input scene consisting of cluttered objects, our method first constructs a target layout of objects as a guidance to the robot for arranging them. For constructing the layout, we use positive examples and pre-extract hierarchical, spatial and pairwise relationships between objects, to understand the user preference on arranging objects. Our method then enables a robot to arrange input objects to reach their target configurations using any task and motion planner. To efficiently arrange the objects, we also propose a priority layer that decides an order of arranging objects to take a small amount of actions. This is achieved by utilizing a dependency graph between objects. We test our method in three different scenes with varying numbers of objects, and apply our method to two well-known task and motion planners with the virtual PR2 robot. We demonstrate that we can use the robot to automatically arrange objects, and show that our priority layer reduces the total running time up to 2.15 times in those tested planners.