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16 June 2023 Learning separate objects in clutter with deep reinforcement learning
Jiangming Li, Xinyan Li, Hao Li
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Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 127022T (2023) https://doi.org/10.1117/12.2680487
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China
Abstract
When the robot grasps the objects in the stack, it fails because the objects block each other and cannot find the appropriate grasping point and grasping path. Humans usually separate objects from each other before grasping stacked objects, so how to let robots effectively separate objects is crucial to achieve stable grasping. At present, many traditional analysis methods have problems such as poor generalization effect and difficulty in establishing appropriate mathematical models. Therefore, a method based on deep reinforcement learning is proposed, which trains the robot end-to-end in the continuous action space of the robot, inputs the depth map of the scene, and outputs the robot's push action. At the same time, an algorithm for judging the separation degree of a pile of objects is proposed to judge the separation degree of objects before and after the push action. The experimental results show that our method can effectively separate a pile of objects.
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Jiangming Li, Xinyan Li, and Hao Li "Learning separate objects in clutter with deep reinforcement learning", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 127022T (16 June 2023); https://doi.org/10.1117/12.2680487
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KEYWORDS
Machine learning
Deep learning
Clutter
Mathematical modeling
Neural networks
Scientific research
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