Organizations: Hong Kong Embodied AI Lab, Hong Kong SAR, China · The Chinese University of Hong Kong, Hong Kong SAR, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China
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∘ 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.
Figures & tables
Fig. 1: Biological inspiration and robotic implementation. (A) Cross jelly ( Mitrocoma cellularia ), showing the compliant bell, radial canals, and densely distributed marginal tentacles. (B) Proposed tendon-driven jellyfish-inspired robot, with eight radial members supporting the compliant silicone bell. Scale bar: 10 cm.
Fig. 2: Mechanical architecture and actuator construction. (A) Robot 3D design model showing the bell, servomotors, radial members, electronics, and ballast. (B) Laminated radial member with a TPU 90A substrate [ 19 ] , a PLA Basic ossicle strip [ 20 ] , and a distal membrane clip. (C) Flexural links and contact geometry; scale bar: 4 mm. The laminate combines localised bending with elastic recovery and geometric rotation limits.
Fig. 3: Bending geometry and characterisation. (A) Total bend angle θ and start-to-tip angle ϕ versus motor take-up, with geometric definitions. Central curves follow the 3D design model, while the accompanying deviations reflect physical measurements. (B) Representative configurations spanning 0 to 29.72mm take-up and 0∘ to approximately 150∘ total bend. The geometry and measurements support a quasi-linear displacement-bending relationship.
Fig. 4: Tendon force during contraction and recovery in water and air. Shading indicates one standard deviation about the mean. The largest recorded forces were 27.6N in water and 24.5N in air, a 3.1N increase (approximately 13% ). The comparison highlights path-dependent loading and the higher peak load during submerged actuation.
Fig. 5: Prescribed open-loop actuation organisation and swimming cycle. (A) Four servomotors each actuate a neighbouring pair of radial members. (B) Representative bell configurations and schematic displacement traces during consecutive cycles. Shared stroke timing permits amplitude asymmetry while preserving phase synchronisation.
Fig. 6: Cycle-synchronous RL-based depth control. Depth and inertial feedback provide the observation ok ; the actor selects uk , which is mapped to a common amplitude Ak for four synchronous servomotors and held throughout the stroke. Both networks share the same observation during training. Simulation rollouts, the critic, reward evaluation, and PPO optimisation are training-only components; only the actor is deployed.
Fig. 7: Physical swimming behaviours under prescribed open-loop actuation. (A) Representative synchronous ascent sequence. (B) Pressure-derived upward displacement during the representative ascent trial. (C) Representative asymmetric steering sequence. (D) Zero-referenced roll, pitch, and yaw evolution during the illustrated manoeuvre. (E) Recovery from inversion followed by upward motion. (F) Self-righting swimming. Righting occurred during continued pulsation without learned attitude control.
Fig. 8: Representative closed-loop depth-control behaviour using the common-amplitude PPO policy. (A) Simulated swimming sequence. (B) Corresponding simulated depth. (C) Physical deployment of the same policy. (D) Corresponding pressure-derived physical depth response. Annotated RMSEs refer to the illustrated trajectories rather than multi-trial aggregates. These examples show closed-loop depth regulation in simulation and after transfer to the physical robot.
Soft robots are valuable robophysical platforms for studying body-caudal undulatory locomotion, but their compliant bodies are difficult to control precisely under changing hydrodynamic loading. Conventional proportional-integral-derivative (PID) feedback stabilizes periodic undulation in static water, but can accumulate flow-dependent tracking delay and increasing inter-trial variability when environmental flow becomes non-trivial. Here, we evaluate whether augmenting PID control with a Linear Repetitive Learning Estimation Scheme (PID-LRLES) recovers tracking accuracy and repeatability under dynamic flow. The LRLES generalizes classical integral action from constant to periodic, non-constant references, while using a stable transfer-function realization whose poles have negative real parts to avoid the long-term instability issues of classical repetitive control. Closed-loop experiments were carried out in a recirculating flow tank at five bulk flow speeds spanning 0 to 32.6 cm s^-1, using an embedded soft capacitive bending sensor at a 1 kHz control-loop rate. With controller gains tuned once in static water and then held fixed across all conditions, PID-LRLES tracked the periodic bending-envelope reference more closely than the PID baseline and significantly reduced the inter-trial spread of the per-trial RMSE (paired Wilcoxon signed-rank test, p = 1.8 x 10^-4, n = 25). Embedded soft proprioception and cycle-to-cycle learning act as complementary contributors to robustness: the sensor exposes the periodic hydrodynamic bias in body deformation, while the learning term absorbs it over recent oscillation cycles. By reducing flow-dependent control-induced variability, the approach provides an enabling layer for future robophysical studies seeking to isolate the effects of morphology, sensing, and environmental flow on aquatic locomotion.
Fabian Schwab, Federico Allione, Bingcheng Wang +5
1Engineering Sciences Department, Swiss Federal Laboratories for Materials Science and Technology, Switzerland · Institute for NeuroInformatics, University of Zurich, Switzerland · Department of Electronic Engineering, University of Rome Tor Vergata, Italy
Complex tasks for underwater robots remain limited by the capabilities of their controllers. Learning a better one for a soft, underactuated robotic fish trades simulator cost against fidelity. We show that an intentionally low-fidelity simulator is enough: a stateless, quasi-steady fluid model with no wake and no added-mass history suffices to learn a \emph{general}, closed-loop controller that transfers to hardware without tuning. Our platform is a soft, single-motor, tendon-driven fish whose policy observes only what the hardware can measure. A staged pipeline grounds the simulator in two independent identifications, fixing the tail dynamics and a stateless fluid model; the policy then acts through a band-limited rhythmic trajectory generator rather than commanding the tail directly. Deployed unchanged in an outdoor pool, a single policy performs closed-loop target reaching, disturbance rejection, and out-of-distribution target acquisition and tracking. The transfer rests on the constraint rather than the fidelity: the generator cannot leave the band over which the fluid was identified. This raises the question of how much of the physics can reside in the controller rather than in the simulator.
Liam Maloney, Simon Ramchandani, Mike Y. Michelis +2
Soft Robotics Lab, ETH Zurich, Switzerland · ETH AI Center, ETH Zurich, Switzerland
Increasing interest in deep-sea operations and resources motivates the development of ecologically sensitive but environmentally durable robots. Dielectric elastomer actuator artificial muscles are good candidates for powering such systems due to their pressure and temperature tolerance and soft makeup, but they are difficult to integrate with robotic systems. This work presents an autonomous robotic platform: the CORE, capable of driving six artificial muscles while sensing visual and spatial information. To validate the platform, we developed the Cuttlebot - a cuttlefish-inspired robot that swims in three dimensions using undulatory fin locomotion. The Cuttlebot has four primary artificial muscles in its fins in addition to a tentacle-inspired soft gripper. The robot was evaluated in a series of tethered and untethered swimming tests, demonstrating a top speed of 2.5 centimeters per second translation and 10 degrees per second rotation. Furthermore, the CORE system was capable of driving specialized control signals into the artificial muscles to controllably output force and torque in six axes. This work provides a platform for developing complex, bio-inspired swimming robots for ocean exploration and monitoring, laying the foundation with our leading example: the Cuttlebot.
Alexander Nicholas White, Ang Leo Li, Alexander Yin +5
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut; Storrs, CT, U.S.A. · Department of Mechanical and Industrial Engineering, University of Toronto; Toronto, Ontario, Canada. · University of Connecticut, Institute of Materials Science; Storrs, CT, U.S.A. +1