@inproceedings{781f7b0b9b3042e7858f1ded301845e7,
title = "Adaptive Unknown Object Rearrangement Using Low-Cost Tabletop Robot",
abstract = "Studies on object rearrangement planning typically consider known objects. Some learning-based methods can predict the movement of an unknown object after single-step interaction, but require intermediate targets, which are generated manually, to achieve the rearrangement task. In this work, we propose a framework for unknown object rearrangement. Our system first models an object through a small-amount of identification actions and adjust the model parameters during task execution. We implement the proposed framework based on a low-cost tabletop robot (under 180 USD) to demonstrate the advantages of using a physics engine to assist action prediction. Experimental results reveal that after running our adaptive learning procedure, the robot can successfully arrange a novel object using an average of five discrete pushes on our tabletop environment and satisfy a precise 3.5 cm translation and 5° rotation criterion.",
author = "Chai, {Chun Yu} and Peng, {Wen Hsiao} and Tsao, {Shiao Li}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; null ; Conference date: 31-05-2020 Through 31-08-2020",
year = "2020",
month = may,
doi = "10.1109/ICRA40945.2020.9197356",
language = "English",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2372--2378",
booktitle = "2020 IEEE International Conference on Robotics and Automation, ICRA 2020",
address = "United States",
}