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.

DiffStir's innovative constitutive model combines the Kelvin-Voigt viscoelastic framework with the Neo-Hookean model for volumetric responses, capturing the behaviors of both Newtonian and non-Newtonian fluids, including those characterized by Carreau, Cross, and Herschel-Bulkley models. This integration ensures adherence to Navier-Stokes principles, enabling DiffStir to offer a unified and precise simulation of fluid dynamics across a diverse spectrum of material types and conditions. Specifically tailored for automated processes, this integrated and universal perspective is highly suited for industrial and research applications.

DiffStir employs a differentiable MLS-MPM approach, offering natural collision handling, fluid-solid coupling, and momentum conservation. Under specific conditions, DiffStir's model can be mathematically reduced to the Corotated model. FluidLab has successfully demonstrated the Corotated model's ability to represent various physical phenomena within MLS-MPM simulations, such as Kármán Vortex Street, Dam Break, Magnus Effect, Rayleigh–Taylor Instability, incompressibility, volume stability, and buoyancy. This highlights that DiffStir's observations and understanding of liquids encompass these phenomena in simulation.