Autonomous Particle Image Velocimetry Using Zero-Shot Sim-to-Real Reinforcement Learning
Overview
This research presents a method where a reinforcement learning agent controls a six-degree-of-freedom manipulator equipped with a camera to autonomously position itself for particle image velocimetry (PIV) measurements. Trained entirely in a photorealistic simulation using computational fluid dynamics data, the agent achieves zero-shot transfer to a real water tunnel. It successfully locates and frames hydrofoil wakes generated by varying profiles and angles of attack, resolving flow fields at higher resolution than fixed camera views while reducing measurement time and image pair requirements.
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Environment: simulation
“forcement learning agent to control a 6-DoF manipulator equipped with a camera, enabling it to locate, frame and resolve a hydrofoil wake in a water tunnel. The agent commands the camera pose and observes the scene only through the velocity field and uncertainty estimated from time-separated image pairs. It is trained entirely in a photorealistic simulation of our water tunnel, with wakes generated by Computational F”
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- arXiv Robotics — research abstractsSignalAutonomous Particle Image Velocimetry Using Zero-Shot Sim-to-Real Reinforcement Learning
This research presents a method where a reinforcement learning agent controls a six-degree-of-freedom manipulator equipped with a camera to autonomously position itself for particle image velocimetry (PIV) measurements. Trained entirely in a photorealistic simulation using computational fluid dynamics data, the agent achieves zero-shot transfer to a real water tunnel. It successfully locates and frames hydrofoil wakes generated by varying profiles and angles of attack, resolving flow fields at higher resolution than fixed camera views while reducing measurement time and image pair requirements.
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