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

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

URL: http://arxiv.org/abs/2107.08067v1
Date: Fri, 16 Jul 2021 18:20:58 GMT
Title: DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation
Authors: Bao Thach, Alan Kuntz, Tucker Hermans
Abstract summary: We propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet. We explicitly use 3D point clouds as the state representation and apply Convolutional Neural Network on point clouds to learn the 3D features. Once trained in an end-to-end fashion, DeformerNet directly maps the current point cloud of a deformable object, as well as a target point cloud shape, to the desired displacement in robot gripper position.
Score: 5.733365759103406
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet. Controlling the shape of a 3D object requires an effective state representation that can capture the full 3D geometry of the object. Current methods work around this problem by defining a set of feature points on the object or only deforming the object in 2D image space, which does not truly address the 3D shape control problem. Instead, we explicitly use 3D point clouds as the state representation and apply Convolutional Neural Network on point clouds to learn the 3D features. These features are then mapped to the robot end-effector's position using a fully-connected neural network. Once trained in an end-to-end fashion, DeformerNet directly maps the current point cloud of a deformable object, as well as a target point cloud shape, to the desired displacement in robot gripper position. In addition, we investigate the problem of predicting the manipulation point location given the initial and goal shape of the object.