DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects

arXiv:2305.04449v3 Announce Type: replace-cross
Abstract: Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic models of elastic, 3D deformable objects require numerous parameters to describe the potentially infinite degrees of freedom present in determining the object's shape. Previous attempts at performing 3D shape control rely on hand-crafted features to represent the object shape and require training of object-specific control models. We overcome these issues …

abstract applications arxiv cs.ai cs.lg cs.ro elastic fields freedom fulfillment home manipulation objects parameters robots type warehouse