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arXiv · 2609.29132

A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning

Jiarui Peng·Yutong Wu·Zelong Wang·Ping Deng·Xiaotian Zhang·Xian Chen·Kecheng Qin·Zhongyi Li
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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.

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Jiarui Peng, Yutong Wu, Zelong Wang, Ping Deng, Xiaotian Zhang, Xian Chen, Kecheng Qin, Zhongyi Li. 2026-09-24. A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning. https://arxiv.org/abs/2609.29132

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