Regular Papers

International Journal of Control, Automation and Systems 2021; 19(10): 3428-3434

Published online July 27, 2021

https://doi.org/10.1007/s12555-019-0758-1

© The International Journal of Control, Automation, and Systems

Irregular Depth Tiles: Automatically Generated Data Used for Network-based Robotic Grasping in 2D Dense Clutter

Da-Wit Kim, HyunJun Jo, and Jae-Bok Song*

Korea University

Abstract

Recent advances in deep learning have enabled robots to grasp objects even in complex environments. However, a large amount of data is required to train the deep-learning network, which leads to a high cost in acquiring the learning data owing to the use of an actual robot or simulator. This paper presents a new form of grasp data that can be generated automatically to minimize the data-collection cost. The depth image is converted into simplified grasp data called an irregular depth tile that can be used to estimate the optimal grasp pose. Additionally, we propose a new grasping algorithm that employs different methods according to the amount of free space in the bounding box of the target object. This algorithm exhibited a significantly higher success rate than the existing grasping methods in grasping experiments in complex environments.

Keywords Data generation, deep learning, grasping, manipulation.

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(10): 3428-3434

Published online October 1, 2021 https://doi.org/10.1007/s12555-019-0758-1

Copyright © The International Journal of Control, Automation, and Systems.

Irregular Depth Tiles: Automatically Generated Data Used for Network-based Robotic Grasping in 2D Dense Clutter

Da-Wit Kim, HyunJun Jo, and Jae-Bok Song*

Korea University

Abstract

Recent advances in deep learning have enabled robots to grasp objects even in complex environments. However, a large amount of data is required to train the deep-learning network, which leads to a high cost in acquiring the learning data owing to the use of an actual robot or simulator. This paper presents a new form of grasp data that can be generated automatically to minimize the data-collection cost. The depth image is converted into simplified grasp data called an irregular depth tile that can be used to estimate the optimal grasp pose. Additionally, we propose a new grasping algorithm that employs different methods according to the amount of free space in the bounding box of the target object. This algorithm exhibited a significantly higher success rate than the existing grasping methods in grasping experiments in complex environments.

Keywords: Data generation, deep learning, grasping, manipulation.

IJCAS
February 2025

Vol. 23, No. 2, pp. 359~682

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