Approaching a target position and holding station in flowing water is a fundamental and critical capability for robotic fish operating in natural aquatic environments. Despite decades of advances in enhancing swimming efficiency and maneuverability, this capability remains underdeveloped, largely owing to the insufficiently characterized, highly nonlinear fluid-structure interactions inherent to freely swimming robotic fish in flows. To bridge this gap, we propose the SWiFT framework, a Swimming With Flow Toolbox that enables the efficient exploration of an egocentric station-holding policy for a body and/or caudal fin (BCF) robotic fish in unknown and turbulent background flows via reinforcement learning (RL). Our SWiFT integrates a free-swimming flow-tank experimental setup with a highly efficient, physically consistent computational fluid dynamics (CFD)-based simulator and a systematic sim-to-real transfer pipeline. The resulting policy achieves substantial improvements over state-of-the-art methods across all metrics, most notably root-mean-square error (RMSE) of distance. Furthermore, we validated that egocentric feedback alone, without any explicit flow sensing, enables station-holding in unknown turbulent flows, closely mirroring the biological phenomenon of rheotaxis. Accordingly, the success of this egocentric station-holding policy not only advances robotic fish control toward real-world deployment, but also highlights SWiFT's promise as a foundation for tackling complex swimming tasks for underwater robots.
A fully computational framework focusing on the modeling and simulation of a spatio-temporal sensory system to autonomously generate the Kármán gait is proposed, providing a robust algorithmic blueprint for future physical deployments in complex aquatic environments.
Xin-Qi Wang, Ming Wang, Xin-Yan Liu et al.· Bioinspiration & Biomimetics· 0 citations
Yaw regulation of biomimetic underwater robots is complicated by flexible body motion, nonlinear hydrodynamics, and coupled actuation. This study examines whether a policy trained in simulation can be deployed on an existing robotic sea lion (RSL) without changing its hardware or low-level controllers. A deep deterministic policy gradient (DDPG) controller was formulated from measurable states and available actuator commands and trained in Webots using a model calibrated from previous tank tests. Four manually selected reward-weight settings and command update rates of 1, 2, 5, and 10 Hz were examined as deployment-oriented sensitivity comparisons, after which a 5 Hz policy was evaluated in six tank trials. Performance was reanalyzed using the circular-angle mean absolute error (MAE) and root mean square error (RMSE). In the two straight-swimming trials, the per-trial MAE was 1.01–1.12°, and the RMSE was 1.10–1.46°. In the four turning trials, evaluated from the first target crossing to the end of each record, the MAE was 1.71–3.63°, the RMSE was 2.10–4.20°, and the maximum overshoot was 2.99–7.70°. Despite the transient differences between simulations and experiments, the controller regulated the robot toward the target headings in all six tank trials. These results demonstrate the successful sim-to-real deployment of reinforcement-learning-based yaw control under low-frequency communication constraints and provide experimental evidence for its application to biomimetic underwater robots.
Ze-Yi Zhang, Yu-Hong Liu, Shuang-Tao Liu et al.· Journal of Marine Science an...· 0 citations
Achieving high-speed locomotion in quadrupedal robots remains highly challenging, as actuators operate near their physical limits and exhibit pronounced nonlinearities. However, many existing methods neglect actuator nonlinearities and physical constraints during training, leading to a significant sim-to-real gap under highly dynamic motions and limiting achievable performance. To address this issue, we propose a high-speed locomotion framework that reduces sim-to-real discrepancies and stabilizes learning over a wide command distribution. A refined actuator model explicitly captures high-speed voltage coupling and magnetic saturation, enabling a more accurate representation of the torque-speed envelope. In addition, a reinforcement learning framework incorporating a two-stage curriculum and adaptive command scheduling (ACS) ensures stable training. Experiments on the 36.5 kg quadruped BlackPanther2 (BP2) demonstrate speeds of up to 13.2 m/s on a treadmill and 11.65 m/s outdoors, establishing a new state-of-the-art and, to the best of our knowledge, a world record for quadrupedal robot locomotion. The results further highlight the importance of accurate actuator modeling in preventing non-physical policy exploitation, and show that ACS improves robustness without sacrificing performance.
Yu-Cheng Tao, Shao-wen Cheng, Guo-Rong Lan et al.· IEEE Robotics and Automation...· 0 citations
Hydrodynamic models underpin Autonomous Underwater Vehicle (AUV) design, motion control, and performance evaluation. Existing methods face two critical bottlenecks: (1) conventional explicit CFD requires predefined trajectories, which fails to capture true motion responses under combined rudder-propeller action and creates a disconnect between simulation and real operations; (2) the widely adopted Standard Submarine Motion Equations (SSME) suffer from high parameter redundancy, while high-precision non-parametric models incur prohibitive computational costs, hindering embedded deployment. To address these gaps, this paper proposes an implicit CFD-driven framework for fully appended AUVs equipped with through-body thrusters. It requires no preset trajectories, directly coupling periodic propeller thrust and rudder angle excitations to achieve 5-degree-of-freedom (5DOF) spatial motion simulations aligned with real navigation states. Parametric and non-parametric models are identified via Least Squares (LS) and Neural Networks (NN), respectively. Sobol global sensitivity analysis reduces SSME dimensionality, yielding a Basic Submarine Motion Equation (BSME) with only 25 key parameters—cutting the parameter count by 55% with negligible accuracy loss. Validation shows the non-parametric NN model reduces prediction error by over 10% compared to its parametric counterpart, while the streamlined BSME enables real-time forecasting in low-power computing scenarios. This approach balances accuracy and efficiency for rapid hydrodynamic prediction during early AUV design and embedded controller deployment.
Yingjie Guan, Xiao-Yang Deng, Yougang Bian et al.· Journal of Marine Science an...· 0 citations
Many fish species are capable of leaping out of water, and several robotic fish have been developed to mimic this behavior. However, enhancing jumping performance and achieving low‐altitude gliding—as seen in flying fish—remain challenging. This study presents a bionic flying‐fish robot capable of efficient high‐speed swimming and water‐exiting motion. Its morphology is reconstructed from a 3D scan of a flying fish, a representative cross‐medium organism. Unlike conventional jumping robotic fish, the proposed design features an optimized body shape and an improved tail‐drive mechanism, which reduce kinetic energy loss during high‐frequency oscillation. Experimental tests and analyses of the Strouhal number (St) and Reynolds number (Re) confirm efficient propulsion and swimming characteristics comparable to real fish, with a maximum speed of 2.54 m/s. The simplified pectoral‐fin structure further enables agile steering and pitching control. During high‐speed swimming, the Froude number (Fr) exceeds 1, demonstrating the robot's ability to fully exit the water. In the test, the robotic fish was able to leap out of the water to a height of 0.5–0.55 m at a sprint angle of to . These results highlight the robot's exceptional locomotion performance and provide insights for the design of cross‐medium robotic systems.
Jiang-Jiang Chen, Haozhi Chen, Kexian Liu et al.· Journal of Field Robotics· 0 citations
Biological swimmers and flyers exploit unsteady vortices for propulsion, whereas engineered vehicles usually suppress them as disturbances. Learning such flow exploitation in machines is difficult because real-fluid interaction data are scarce and unstructured exploration is unstable in high-dimensional, history-dependent flows. Here we present REEF, a co-designed physical-learning framework that integrates SHOAL, an eight-channel high-throughput array for real fluid--structure interaction, with V-STAR, a staged algorithm that converts these interactions into policies through imitation, offline internalization, and online adaptation. Across lift-based, drag-based, and momentum-jet propulsors, REEF expands the attainable force envelope to more than twice that of parameterized search. Particle image velocimetry shows that these gains arise from coordinated vortex formation, growth, and force projection, rather than refinement of a fixed motion-to-force mapping. Force-trained policies transfer zero-shot to free-moving robots whose body motion changes the surrounding flow, suggesting that REEF learns transferable wake-coupling principles for embodied propulsion in unsteady fluids.
Fei Han, Xin-Yu Cui, Zhi-Peng Wang et al.· 0 citations
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