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