Automated task planning using object arranement optimization

by Mincheul Kang, Youngsun Kwon, and Sung-Eui Yoon
International Conference on Ubiquitous Robots(UR), 2018

This shows an overview of our approach. Our method first constructs an arranged scene as the target layout (b) from the initial state (a) using various relationships extracted from user' positive examples. At runtime, we use our priority layer, considering the computed layout and various relationship between objects defined in a dependency graph, for a robot to arrange objects by using a task and motion planner (TMP). The priority layer communicate with TMP (c). We repeat to use TMP with our priority layer until all the objects are arrived at their goal state (d).

Abstract

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.

Video

Contents

Paper: PDF (1.91MB)
Source Code: ZIP file, Github page

School of Computing
KAIST
Daehak-ro 291, Guseong-dong, Yuseong-gu, Daejeon, 305-701
South Korea
mincheul.kang (at) kaist.ac.kr