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The growing interest in the flexibility and operational capabilities of soft manipulators in confined spaces emphasizes the need for precise modeling and accurate motion control. 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 8–15 mm 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.

The ocean environment has enormous uncertainty due to the influence of complex waves and undercurrents. The human beings are limited in their abilities to detect and utilize marine resources without powerful tools. Soft robots employ soft materials to simplify the complex mechanical structures in rigid robots and adapt their morphology to the environment, making them suitable for performing some challenging tasks in place of manual labor. Due to superior flexible and deformable bodies, underwater soft robots have played significant roles in numerous applications in recent decades. Meanwhile, various technical challenges still need to be tackled to ensure the reliability and practical performance of underwater soft robots in complicated ocean environment. Nowadays, some researchers have developed underwater soft robotic systems based on biomimetics and other disciplines, aiming at comprehensive exploration of ocean and appropriate utilization of unexploited resources. This review presents the recent advances of underwater soft robots in the aspects of intelligent soft materials, fabrication, actuation, locomotion patterns, power storage, sensing, control, and modeling; additionally, the existing challenges and perspectives are analyzed as well.

Objective: To summarize the clinical experience of Professor Liangduo Jiang in the prevention and control of the new coronavirus disease COVID-19. Methods: Between January 2020 and February 2020, 87 subjects who have had contact with patients tested positive for COVID-19 in an isolated observation site in Tianjin were given the Chinese medicine prescription for prevention prescribed by Professor Liangduo Jiang continuously for three days. Their basic information, clinical manifestations and medication adherence were statistically analyzed, and the clinical manifestations, temperature changes and infection status after medication were summarized. Results: After the 14-day isolation observation period, only one patient who had close contact with confirmed patients developed a positive test for COVID-19 after taking the preventive Chinese medicine prescription, with the infection rate of 1.149%. Conclusion: The preventive treatment by Professor Liangduo Jiang is effective in the prevention and control of the epidemic.