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Conventional control methods encounter difficulties in modeling and involve intricate computations. This work introduces a novel deep reinforcement learning (DRL) control algorithm based on neural network modeling. Using the Whale Optimization Algorithm, an approximate dynamic model for the soft manipulator is established. The twin delayed deterministic policy gradient is employed for DRL control. Domain randomization is applied during pretraining in a simulated environment. The algorithm addresses issues related to dependency on measurement data quality and redundant mappings, outperforming other methods by 8\u201315\u2009mm in control accuracy. The trained DRL controller achieves precise trajectory tracking within the soft manipulator's task space, enabling successful grasping tasks in various complex environments, including pipelines and other narrow spaces. Experimental results confirm the autonomy of our controller in performing these tasks without human intervention.<\/jats:p>","DOI":"10.1002\/aisy.202400112","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:29:16Z","timestamp":1724920156000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Cable\u2010Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning"],"prefix":"10.1002","volume":"6","author":[{"given":"Kunyu","family":"Zhou","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China"},{"name":"Shenzhen Key Laboratory of Advanced Technology for Marine Ecology Tsinghua University Shenzhen 518055 China"}]},{"given":"Baijin","family":"Mao","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School Tsinghua University Shenzhen 518055 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