Mimicking the graceful motion of swimming animals remains a core challenge in soft robotics due to the complexity of fluid-structure interaction and the difficulty of controlling soft, biomimetic bodies. Existing modeling approaches are often computationally expensive and impractical for complex control or reinforcement learning needed for realistic motions to emerge in robotic systems. In this work, we present a tendon-driven fish robot modeled in an efficient underwater swimmer environment using a simplified, stateless hydrodynamics formulation implemented in the widespread robotics framework MuJoCo. With just two real-world swimming trajectories, we identify five fluid parameters that allow a matching to experimental behavior and generalize across a range of actuation frequencies. We show that this stateless fluid model can generalize to unseen actuation and outperform classical analytical models such as the elongated body theory. This simulation environment runs faster than real-time and can easily enable downstream learning algorithms such as reinforcement learning for target tracking, reaching a 93% success rate. Due to the simplicity and ease of use of the model and our open-source simulation environment, our results show that even simple, stateless models -- when carefully matched to physical data -- can serve as effective digital twins for soft underwater robots, opening up new directions for scalable learning and control in aquatic environments.

翻译：模仿游泳动物的优雅运动仍然是软体机器人领域的核心挑战，这源于流固耦合的复杂性以及控制柔软仿生躯体的困难。现有的建模方法通常计算成本高昂，对于在机器人系统中实现逼真运动所需的复杂控制或强化学习而言并不实用。在本研究中，我们提出了一种肌腱驱动鱼形机器人模型，该模型在一个高效的水下游动环境中实现，采用了简化的无状态流体动力学公式，并部署于广泛使用的机器人框架MuJoCo。仅使用两条真实世界的游动轨迹，我们识别出五个流体参数，使模型能够匹配实验行为，并在一系列驱动频率下保持泛化能力。我们证明，这种无状态流体模型能够泛化至未见过的驱动模式，且性能优于经典解析模型（如细长体理论）。该仿真环境运行速度快于实时，可轻松支持下游学习算法（如用于目标跟踪的强化学习），成功率可达93%。由于模型及我们开源仿真环境的简洁性与易用性，研究结果表明，即便是简单的无状态模型——只要与物理数据仔细匹配——也能作为软体水下机器人的有效数字孪生，为水生环境中可扩展的学习与控制开辟新方向。