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In this paper, we propose a novel approach to 3D deformable objectmanipulation leveraging a deep neural network called DeformerNet. Controllingthe shape of a 3D object requires an effective state representation that cancapture the full 3D geometry of the object. Current methods work around thisproblem by defining a set of feature points on the object or only deforming theobject in 2D image space, which does not truly address the 3D shape controlproblem. Instead, we explicitly use 3D point clouds as the state representationand apply Convolutional Neural Network on point clouds to learn the 3Dfeatures. These features are then mapped to the robot end-effector's positionusing a fully-connected neural network. Once trained in an end-to-end fashion,DeformerNet directly maps the current point cloud of a deformable object, aswell as a target point cloud shape, to the desired displacement in robotgripper position. In addition, we investigate the problem of predicting themanipulation point location given the initial and goal shape of the object.

DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation