Egocentric Station Holding of Robotic Fish in Unknown Turbulent Background Flow
Researchers propose the SWiFT framework enabling robotic fish to hold station in unknown turbulent flows using egocentric feedback and reinforcement learning, achieving significant improvements over existing methods.
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[Submitted on 26 Jul 2026]
Title:Egocentric Station Holding of Robotic Fish in Unknown Turbulent Background Flow
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Abstract: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.
Comments: 20 pages, 18 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.24860 [cs.RO]
(or arXiv:2607.24860v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.24860
arXiv-issued DOI via DataCite
Submission history
From: Xiaozhu Lin [view email] [v1] Sun, 26 Jul 2026 12:14:03 UTC (17,496 KB)
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