Challenges and Outlook in Robotic Manipulation of Deformable Objects

Journal article (2022)

Authors

J. Zhu Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Andrea Cherubini Université Montpellier II
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Claire Dune Université de Toulon
David Navarro-Alarcon The Hong Kong Polytechnic University
Farshid Alambeigi The University of Texas at Austin
Dmitry Berenson University of Michigan
Fanny Ficuciello Università degli Studi di Napoli Federico II
Kensuke Harada Osaka University
J. Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Wenzhen Yuan Carnegie Mellon University
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 J. Zhu, Andrea Cherubini, Claire Dune, David Navarro-Alarcon, Farshid Alambeigi, Dmitry Berenson, Fanny Ficuciello, Kensuke Harada, J. Kober, Wenzhen Yuan, More Authors
DOI
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https://doi.org/10.1109/MRA.2022.3147415
Strain Sensors Robots Soft robotics Robot sensing systems Task analysis Grippers
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and health-care sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex and is still an open research problem. Addressing DOM challenges demands breakthroughs in almost all aspects of robotics: hardware design, sensing, (deformation) modeling, planning, and control. In this article, we review recent advances and highlight the main challenges when considering deformation in each subfield. A particular focus of our article lies in the discussions of these challenges and proposing future directions of research.