Touch if it's Transparent! ACTOR: Active Tactile-Based Category-Level Transparent Object Reconstruction

Murali, P. K. , Porr, B. and Kaboli, M. (2023) Touch if it's Transparent! ACTOR: Active Tactile-Based Category-Level Transparent Object Reconstruction. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), Detroit, MI, USA, 1-5 October 2023, pp. 10792-10799. ISBN 9781665491914 (doi: 10.1109/iros55552.2023.10341680)

Abstract

Accurate shape reconstruction of transparent ob-jects is a challenging task due to their non-Lambertian surfaces and yet necessary for robots for accurate pose perception and safe manipulation. As vision-based sensing can produce erroneous measurements for transparent objects, the tactile modality is not sensitive to object transparency and can be used for reconstructing the object's shape. We propose AC-TOR, a novel framework for ACtive tactile-based category-level Transparent Object Reconstruction. ACTOR leverages large datasets of synthetic object with our proposed self-supervised learning approach for object shape reconstruction as the collection of real-world tactile data is prohibitively expensive. ACTOR can be used during inference with tactile data from category-level unknown transparent objects for reconstruction. Furthermore, we propose an active-tactile object exploration strategy as probing every part of the object surface can be sample inefficient. We also demonstrate tactile-based category-level object pose estimation task using ACTOR. We perform an extensive evaluation of our proposed methodology with real-world robotic experiments with comprehensive comparison studies with state-of-the-art approaches. Our proposed method outperforms these approaches in terms of tactile-based object reconstruction and object pose estimation.

• Item Type: Conference Proceedings
• Additional Information: Funded in part by the BMW Group, EU H2020 INTUITIVE under Grant ID 861166 and EU Horizon PHASTRAC under Grant ID 101092096.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porr, Dr Bernd and Murali, Prajval Kumar
• Authors: Murali, P. K., Porr, B., and Kaboli, M.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Publisher: IEEE
• ISSN: 2153-0866
• ISBN: 9781665491914
• Copyright Holders: Copyright © 2023 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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