Problem
Research questions and friction points this paper is trying to address.
Underwater Robot Learning
Sim-to-Real Transfer
High-fidelity Simulation
Hydrodynamics
Multi-sensor Fusion
FinsSim: A Reality-Aligned Integrated Simulation Platform for Underwater Robot Learning
🤖 AI Summary
本文介绍了FinsSim，一个用于水下机器人学习的高保真仿真平台，通过多传感器融合、精确的动力学模型等方法解决仿真到实际应用的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
high-fidelity hydrodynamics
multi-sensor fusion
calibrated thruster-hydrodynamics models
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