Abstract
In this paper, we focus on the robot grasping problem with parallel grippers using image data. For this task, we propose and implement an end-to-end approach. In order to detect the good grasping poses for a parallel gripper from RGB images, we have employed transfer learning for a Convolutional Neural Network (CNN) based object detection architecture. Our obtained results show that, the adapted network either outperforms or is on-par with the state-of-the art methods on a benchmark dataset. We also performed grasping experiments on a real robot platform to evaluate our method's real world performance.
Keywords
GrippersArtificial intelligenceGRASPComputer scienceObject detectionConvolutional neural networkBenchmark (surveying)RobotComputer visionFocus (optics)Task (project management)Object (grammar)Deep learningTransfer of learningRGB color modelCognitive neuroscience of visual object recognitionPattern recognition (psychology)Engineering
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Publication Info
- Year
- 2019
- Type
- article
- Pages
- 4953-4959
- Citations
- 122
- Access
- Closed
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Cite This
Hakan Karaoğuz,
Patric Jensfelt
(2019).
Object Detection Approach for Robot Grasp Detection.
, 4953-4959.
https://doi.org/10.1109/icra.2019.8793751
Identifiers
- DOI
- 10.1109/icra.2019.8793751