期刊
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
卷 -, 期 -, 页码 7300-7306出版社
IEEE
DOI: 10.1109/icra40945.2020.9197179
关键词
-
资金
- Technology Innovation Program or Industrial Strategic Technology Development Program [10077533, 20005062]
- Ministry of Trade, Industry & Energy (MOTIE, Korea)
Applications of deep neural network (DNN) based object and grasp detections could be expanded significantly when the network output is processed by a high-level reasoning over relationship of objects. Recently, robotic grasp detection and object detection with reasoning have been investigated using DNNs. There have been efforts to combine these multi-tasks using separate networks so that robots can deal with situations of grasping specific target objects in the cluttered, stacked, complex piles of novel objects from a single RGB-D camera. We propose a single multi-task DNN that yields accurate detections of objects, grasp position and relationship reasoning among objects. Our proposed methods yield state-of-the-art performance with the accuracy of 98.6% and 74.2% with the computation speed of 33 and 62 frame per second on VMRD and Cornell datasets, respectively. Our methods also yielded 95.3% grasp success rate for novel object grasping tasks with a 4-axis robot arm and 86.7% grasp success rate in cluttered novel objects with a humanoid robot.
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