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

Perceiving and handling deformable objects is an integral part of everyday life for humans. Automating taskssuch 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 controland planning, can provide viable solutions to these open challenges. In addition, with the development of bettersimulation environments, we can generate and study scenarios that allow for benchmarking of various approachesand gain better understanding of what theoretical developments need to be made and how practical systems canbe implemented and evaluated to provide flexible, scalable, and robust solutions. To this end, we survey morethan 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 integratemodel priors and task data in perceiving and manipulating a variety of deformable objects. ; QC 20210804