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.
Original language | English |
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Pages (from-to) | 3428-3434 |
Number of pages | 7 |
Journal | International Journal of Control, Automation and Systems |
Volume | 19 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2021 Oct |
Bibliographical note
Funding Information:This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 20008613).
Publisher Copyright:
© 2021, ICROS, KIEE and Springer.
Keywords
- Data generation
- deep learning
- grasping
- manipulation
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications