发表机构
Soft Robotics Lab, ETH Zurich; ETH AI Center, ETH Zurich（苏黎世联邦理工学院软体机器人实验室; 苏黎世联邦理工学院人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文证明低保真无状态流体仿真器足以零样本训练软体机器鱼闭环控制器，无需调参即可迁移至硬件，实现目标到达、扰动抑制和分布外跟踪，并提出物理可驻留于控制器的观点。
AI 中文摘要
水下机器人的复杂任务仍受限于其控制器的能力。为软体、欠驱动的机器鱼学习更好的控制器，需要在仿真器成本与保真度之间进行权衡。我们证明，一个刻意采用低保真度的仿真器便已足够：一个无状态、准稳态的流体模型，既无尾流也无附加质量历史，足以学习一个通用的闭环控制器，且无需调参即可迁移到硬件。我们的平台是一条软体、单电机、肌腱驱动的鱼，其策略仅观察硬件可测量的量。一个分阶段的流程通过两次独立的辨识来校准仿真器，分别确定尾部动力学和一个无状态流体模型；随后，策略通过一个带限的节律轨迹生成器来执行动作，而非直接指挥尾部。在室外泳池中未经修改地部署，单一策略即可执行闭环目标到达、扰动抑制以及分布外目标的获取与跟踪。这种迁移依赖于约束而非保真度：生成器不能超出流体被辨识的频带。这引发了一个问题：有多少物理特性可以驻留在控制器中，而非仿真器中。
英文摘要
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.
Comments8 pages, 10 figures, submitted to ICRA 2027. Liam Maloney and Simon Ramchandani contributed equally
