A Cable‐Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning

Cable‐Actuated Soft Manipulator Based on Deep Reinforcement Learning In article number 2400112, Juntian Qu and co‐workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short‐term memory) neural networks to control the cable‐...

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Bibliographic Details
Published in:Advanced intelligent systems 2024-10, Vol.6 (10), p.n/a
Main Authors: Zhou, Kunyu, Mao, Baijin, Zhang, Yuzhu, Chen, Yaozhen, Xiang, Yuyaocen, Yu, Zhenping, Hao, Hongwei, Tang, Wei, Li, Yanwen, Liu, Houde, Wang, Xueqian, Wang, Xiaohao, Qu, Juntian
Format: Article
Language:English
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Summary:Cable‐Actuated Soft Manipulator Based on Deep Reinforcement Learning In article number 2400112, Juntian Qu and co‐workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short‐term memory) neural networks to control the cable‐driven soft manipulator. Multi‐scenario and multi‐task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L‐shaped pipe using the soft manipulator.
ISSN:2640-4567
2640-4567
DOI:10.1002/aisy.202470046