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Medientyp:
E-Artikel
Titel:
Research on Perception and Control Technology for Dexterous Robot Operation
Beteiligte:
Zhang, Tengteng;
Mo, Hongwei
Erschienen:
MDPI AG, 2023
Erschienen in:
Electronics, 12 (2023) 14, Seite 3065
Sprache:
Englisch
DOI:
10.3390/electronics12143065
ISSN:
2079-9292
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p>Robotic grasping in cluttered environments is a fundamental and challenging task in robotics research. The ability to autonomously grasp objects in cluttered scenes is crucial for robots to perform complex tasks in real-world scenarios. Conventional grasping is based on the known object model in a structured environment, but the adaptability of unknown objects and complicated situations is constrained. In this paper, we present a robotic grasp architecture of attention-based deep reinforcement learning. To prevent the loss of local information, the prominent characteristics of input images are automatically extracted using a full convolutional network. In contrast to previous model-based and data-driven methods, the reward is remodeled in an effort to address the sparse rewards. The experimental results show that our method can double the learning speed in grasping a series of randomly placed objects. In real-word experiments, the grasping success rate of the robot platform reaches 90.4%, which outperforms several baselines.</jats:p>