一种具有约束软驱动和基于强化学习深度控制的腱驱动仿生水母机器人
A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning
- Hong Kong Embodied AI Lab（香港具身智能实验室）
- The Chinese University of Hong Kong（香港中文大学）
- The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种腱驱动仿生水母机器人，通过约束软驱动实现大变形和线性驱动，并利用强化学习实现闭环深度控制，提升软体机器人的可控性与自主性。
AI中文摘要：
受水母启发的机器人为水下运动提供了一种柔顺且高效的方法，但实现大变形同时保持可重复驱动和闭环控制仍然具有挑战性。在这项工作中，我们提出了一种具有约束软驱动的腱驱动仿生水母机器人。每个驱动器结合柔性基底与离散约束，可实现高达150度的弯曲，并具有近似线性的腱位移-弯曲关系。八个驱动器由四个伺服电机驱动，使机器人能够进行稳定游动、姿态调整和自扶正。基于线性驱动，进一步开发了强化学习控制器，在仿真和物理实验中实现了闭环深度调节。这些结果表明，机械约束可以提高软驱动的可控性，同时保持柔顺的水母样运动，为可操控和自主的水母机器人提供了一条途径。
英文摘要：
Jellyfish-inspired robots offer a compliant and efficient approach to underwater locomotion, but achieving large deformation together with repeatable actuation and closed-loop control remains challenging. In this work, we present a tendon-driven robotic jellyfish with constrained soft actuation. Each actuator combines a flexible substrate with discrete constraints, enabling bending up to \(150^\circ\) with an approximately linear tendon displacement-bending relationship. Eight actuators driven by four servos allow the robot to perform stable swimming, attitude adjustment, and self-righting. Based on the linear actuation, a reinforcement-learning controller is further developed, enabling closed-loop depth regulation in both simulation and physical experiments. These results show that mechanical constraints can improve the controllability of soft actuation while preserving compliant jellyfish-like motion, providing a route toward manoeuvrable and autonomous jellyfish robots.
