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Differentiable Fluid Physics Parameter Identification by Stirring and for Stirring

Wenqiang Xu, Dongzhe Zheng, Yutong Li, Jieji Ren, Cewu Lu

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Abstract

Fluid interactions are crucial in daily tasks, with properties like density and viscosity being key parameters. The property states can be used as control signals for robot op- eration. While density estimation is simple, assessing viscosity, especially for different fluid types, is complex. This study intro- duces a novel differentiable fitting framework, DiffStir, tailored to identify key physics parameters through stirring. Then, given the estimated physics parameters, we can generate commands to guide the robotic stirring. Comprehensive experiments were conducted to validate the efficacy of DiffStir, showcasing its precision in parameter estimation when benchmarked against reported values in the literature. More experiments and videos can be found in the supplementary materials and on the website: https://diffstir.robotflow.ai.

Index terms

Computer Vision for Automation Simulation and Animation Calibration and Identification