Adaptive Unknown Object Rearrangement Using Low-Cost Tabletop Robot

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題2020 IEEE International Conference on Robotics and Automation, ICRA 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2372-2378
頁數7
ISBN(電子)9781728173955
DOIs
出版狀態Published - 五月 2020
事件2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
持續時間: 31 五月 202031 八月 2020

出版系列

名字Proceedings - IEEE International Conference on Robotics and Automation
ISSN(列印)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
國家France
城市Paris
期間31/05/2031/08/20

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