Modeling, learning, perception, and control methods for deformable object manipulation

By Hang Yin, Anastasia Varava, Danica Kragic. Science Robotics, 2021

366 citations recorded by the source corpus.

Enabling robots to handle deformable objects requires careful integration of data-driven and analytic approaches. Perceiving and handling deformable objects is an integral part of everyday life for humans. Automating tasks such as food handling, garment sorting, or assistive dressing requires open problems of modeling, perceiving, planning, and control to be solved. Recent advances in data-driven approaches, together with classical control and planning, can provide viable solutions to these open challenges. In addition, with the development of better simulation environments, we can generate and study scenarios that allow for benchmarking of various approaches and gain better understanding of what theoretical developments need to be made and how practical systems can be implemented and evaluated to provide flexible, scalable, and robust solutions. To this end, we survey more than 100 relevant studies in this area and use it as the basis to discuss open problems. We adopt a learning perspective to unify the discussion over analytical and data-driven approaches, addressing how to use and integrate model priors and task data in perceiving and manipulating a variety of deformable objects.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8c578a8f79f3214b5f6e7e022102aa0cd23c8178

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In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

Challenges and Outlook in Robotic Manipulation of Deformable Objects (2021, background): “Lately, [11] reports most recent advances in modeling, learning, perception, and control in DOM.”