Research Topic

Learning, Sensing and Control for Autonomous Manipulation of Deformable Objects

About this Research Topic

In recent years, the application of robotic technology has been extended from traditional industries (e.g., automotive, electronics, and chemical production) involving rigid objects, to de-industrialized scenarios dealing with Deformable Objects (DOs). One of the technologies required for supporting robots in such scenarios is the ability to autonomously sense and manipulate a DO. This technology is applicable to several economically important fields, such as automated food preparation (e.g., shaping rheological materials), home/personal robotics (e.g., folding fabrics), and surgical robotics (e.g., manipulating organs and tissues in different surgical interventions).

The solid market need for this technology is driving research interests towards developing novel sensor-based methods and algorithms for sensing and manipulation of deformable objects. However, despite the recent progress in DO manipulation, existing methods are still inadequate, in terms of being inclusive, easily adaptable, and robust in complex real-world scenarios. The aim of this Research Topic is to assess the current techniques for DO sensing and manipulation and to provide a useful forum for the current researchers in this topic.

Relevant submissions for this Research Topic include, but are not limited to, the following:
• Vision-based shape sensing/description of 2D/3D deformable objects with high variance in reduced
dimensional feature space
• Sensor-fusion algorithms for online modeling/estimation of deformation behavior
• Nonlinear/High order model representation, estimation, and control of DOs
• DOs shape/position control with multimodal representation
• Shape planning/navigation for DOs
• Reinforcement learning for DOs sensing and manipulation
• Learning-based DOs sensing and manipulation

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Dr. Wang is affiliated with Cornerstone Robotics Limited, a company devoted to developing innovative robotics technology for healthcare with an emphasis on both innovative research and development and practical clinical translation. All other Topic Editors declare no competing interests with regards to the Research Topic subject.


Keywords: Reinforcement Learning, Deformation Modeling, Deformation Estimation, Shape Descriptor, Deformation Control, Shape Planning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In recent years, the application of robotic technology has been extended from traditional industries (e.g., automotive, electronics, and chemical production) involving rigid objects, to de-industrialized scenarios dealing with Deformable Objects (DOs). One of the technologies required for supporting robots in such scenarios is the ability to autonomously sense and manipulate a DO. This technology is applicable to several economically important fields, such as automated food preparation (e.g., shaping rheological materials), home/personal robotics (e.g., folding fabrics), and surgical robotics (e.g., manipulating organs and tissues in different surgical interventions).

The solid market need for this technology is driving research interests towards developing novel sensor-based methods and algorithms for sensing and manipulation of deformable objects. However, despite the recent progress in DO manipulation, existing methods are still inadequate, in terms of being inclusive, easily adaptable, and robust in complex real-world scenarios. The aim of this Research Topic is to assess the current techniques for DO sensing and manipulation and to provide a useful forum for the current researchers in this topic.

Relevant submissions for this Research Topic include, but are not limited to, the following:
• Vision-based shape sensing/description of 2D/3D deformable objects with high variance in reduced
dimensional feature space
• Sensor-fusion algorithms for online modeling/estimation of deformation behavior
• Nonlinear/High order model representation, estimation, and control of DOs
• DOs shape/position control with multimodal representation
• Shape planning/navigation for DOs
• Reinforcement learning for DOs sensing and manipulation
• Learning-based DOs sensing and manipulation

----------------------------------------
Dr. Wang is affiliated with Cornerstone Robotics Limited, a company devoted to developing innovative robotics technology for healthcare with an emphasis on both innovative research and development and practical clinical translation. All other Topic Editors declare no competing interests with regards to the Research Topic subject.


Keywords: Reinforcement Learning, Deformation Modeling, Deformation Estimation, Shape Descriptor, Deformation Control, Shape Planning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 August 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 August 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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