Publication: Category classification of deformable object using hybrid dynamic model for robotic grasping
Date
2019
Authors
Hou Y.C.
Sahari K.S.M.
How D.N.T.
Weng L.Y.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
This work studies the problem of classification of a hung garment in the unfolding procedure by a home service robot. The sheer number of unpredictable configurations that the deformable object can end up in makes the visual identification of the object shape and size difficult. In this paper, we propose a hybrid dynamic model to recognize the pose of hung garment using a single manipulator. A dataset of hung garment is generated by capturing the depth images of real garments at the robotic platform (real images) and also the images of garment mesh model from offline simulation (synthetic images) respectively. Deep convolutional neural network is implemented to classify the category and estimate the pose of garment. Experiment results show that the proposed method performs well and is applicable to different garments in robotic manipulation. � 2019 IEEE.
Description
Agricultural robots; Clothes; Convolutional neural networks; Deep neural networks; Deformation; Dynamic models; Object recognition; Simulation platform; Category Classification; Deformable object; Home service robot; Hybrid dynamic modeling; Off-line simulations; Robotic manipulation; Unfolding procedures; Visual identification; Robotics