Soft robotic manipulators have promising features for performing non-destructive underwater tasks. Nevertheless, soft robotic systems are sensitive to the inherent nonlinearity of soft materials, the underwater flow current disturbance, payload, etc. In this paper, we propose a prediction model-based guided reinforcement learning adaptive controller (GRLMAC) for a soft manipulator to perform spatial underwater grasping tasks. In the GRLMAC, a feed-forward prediction model (FPM) is established for describing the length/pressure hysteresis of a chamber in the soft manipulator. Then, the online adjustment for FPM is achieved by reinforcement learning. Introducing the human experience into the reinforcement learning method, we can choose an appropriate adjustment action for the FPM from the action space without the offline training phase, allowing online adjusting the inflation pressure. To demonstrate the effectiveness of the controller, we tested the soft manipulator in the pumped flow current and different gripping loads. The results show that GRLMAC acquires promising accuracy, robustness, and adaptivity. We envision that the soft manipulator with online learning would endow future underwater robotic manipulation under natural turbulent conditions.

软机器人机械手具有执行无损水下任务的有前途的功能。然而，软机器人系统对软材料的固有非线性、水下流动电流扰动、有效载荷等很敏感。在本文中，我们提出了一种基于预测模型的引导强化学习自适应控制器（GRLMAC），用于软机械手执行空间水下抓取任务。在 GRLMAC 中，建立了前馈预测模型 (FPM) 来描述软机械手中腔室的长度/压力滞后。然后，通过强化学习实现 FPM 的在线调整。将人类经验引入强化学习方法，我们可以在没有离线训练阶段的情况下，从动作空间中为 FPM 选择合适的调整动作，允许在线调整通胀压力。为了证明控制器的有效性，我们在泵送流动电流和不同的夹持负载下测试了软机械手。结果表明，GRLMAC 获得了有希望的准确性、鲁棒性和适应性。我们设想具有在线学习功能的软机械手将赋予未来自然湍流条件下的水下机器人操纵能力。