Differentiable Fluid Physics Parameter Identification By Stirring and For Stirring

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 operation. While density estimation is simple, assessing viscosity, especially for different fluid types, is complex. This study introduces 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.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10801842
Primary Topic
Flow Measurement and Analysis
Type
article
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article

Differentiable Fluid Physics Parameter Identification By Stirring and For Stirring

Jieji Ren, Yutong Li, Wenqiang Xu, Cewu Lu et al.
Flow Measurement and Analysis
article

Differentiable Fluid Physics Parameter Identification By Stirring and For Stirring

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

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 operation. While density estimation is simple, assessing viscosity, especially for different fluid types, is complex. This study introduces 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.

Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 73%
Flow Measurement and Analysis
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