Tendon driven robotic jellyfish swims and controls depth with learning
A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning
Robotics
Summary
Underwater robots inspired by jellyfish could move smoothly and efficiently, but making their flexible parts bend a lot while controlling them precisely is hard. The authors made a robot jellyfish using flexible materials and tendons that bend predictably, driven by a few motors. They used this to make the robot swim and keep steady underwater, including controlling its depth automatically with artificial intelligence. Their work shows how combining simple mechanical parts with learning can improve soft robots that move like jellyfish.
What this means in practice
- •For marine robotics teams: Build agile underwater robots that mimic jellyfish swimming and autonomously control their depth for exploration tasks.
- •For soft robotics engineers: Design soft actuators with mechanical constraints for more predictable bending and easier closed-loop control.
Authors
Jiarui Peng, Yutong Wu, Zelong Wang, Ping Deng, Xiaotian Zhang, Xian Chen, Kecheng Qin, Zhongyi Li
Abstract
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.