发表机构
Shanghai Jiao Tong University; Zhejiang University（上海交通大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FinsSim提出现实对齐的集成仿真平台，通过高保真仿真、多传感器融合定位及校准推进器模型，实现水下机器人控制策略的可靠Sim-to-Real迁移。
AI 中文摘要
水下机器人学习依赖于集成高保真水动力学、便捷学习接口以及向真实场景可信迁移的仿真器。在本工作中，我们提出了FinsSim，一个面向Sim-to-Real（仿真到现实）水下机器人学习的现实对齐集成仿真平台。FinsSim首先构建了具有可选后端的高保真仿真，以适应多样化需求。为促进水下机器人研究，它进一步提供了标准控制基线以及统一机器人学习工作流。为实现可靠的Sim-to-Real迁移，FinsSim采用多传感器融合方案，以低成本提供精确的定位。此外，它实现了校准的推进器水动力学模型和约束力分配算法。通过ROS 2桥接这些模块，FinsSim建立了完整的Sim-to-Real迁移流水线。通过匹配的仿真和实验，证明了使用FinsSim框架可以实现水下机器人控制策略的可靠Sim-to-Real迁移。独立的消融研究也验证了FinsSim的模块可以从不同方面解决水下Sim-to-Real的关键问题。总体而言，本工作旨在弥合理论研究与实际应用之间的差距，最终推动水下机器人领域的发展。
英文摘要
Underwater robot learning relies on simulators that integrate high-fidelity hydrodynamics, convenient learning interfaces, and a credible transition to real scenarios. In this work, we present FinsSim, a reality-aligned integrated simulation platform for Sim-to-Real underwater robot learning. FinsSim first constructs high-fidelity simulation with selectable backends to adapt to diverse requirements. To facilitate underwater robot research, it further offers standard control baselines, alongside with unified robot learning workflows. For reliable Sim-to-Real transfer, FinsSim adopts a multi-sensor fusion scheme to provide low-cost yet precise localization. Moreover, it implements calibrated thruster-hydrodynamics models and a constrained wrench allocation algorithm. Bridging these modules by ROS~2, FinsSim establishes a complete Sim-to-Real transfer pipeline. Through matched simulations and experiments, it is demonstrated that reliable Sim-to-Real transfer of underwater robot control policies can be achieved with the FinsSim framework. Separate ablation studies also validate that the modules of FinsSim can address the pivotal issues of underwater Sim-to-Real from different aspects. Overall, this work aims to bridge the gap between theoretical research and practical applications, ultimately driving advancements in the field of underwater robotics.
Comments8 pages, 6 figures
