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

Da Wit Kim, Hyun Jun Jo, Jae Bok Song

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)3428-3434
Number of pages7
JournalInternational Journal of Control, Automation and Systems
Volume19
Issue number10
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • Data generation
  • deep learning
  • grasping
  • manipulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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