Modeling and Simulation of Single-micropipette Cell Rotation for Imitation Learning

Abstract

Cell rotation plays a crucial role in micromanipulation. Among manual cell rotation techniques, single-micropipette cell rotation is widely adopted due to its high efficiency and flexibility. However, there is currently no method capable of achieving automated single-micropipette cell rotation. In this study, we developed the first three-dimensional (3D) simulation system for single-micropipette cell rotation. Based on this simulation system, we successfully achieved single-micropipette cell rotation imitation learning (IL) for the first time. Specifically, we first analyze the forces acting on cells in the fluid, establishing a dynamic model that describes the cell's behavior in response to the flow velocity at the holding micropipette's orifice, the relative position of the micropipette, and time. We then developed the cell rotation simulation environment by discretizing the model and designing the simulation's cell and holding micropipette models based on real-world conditions. Finally, we designed a network architecture for IL using this model, achieving single-micropipette cell rotation in simulation. The results demonstrate that the simulation system exhibits a relative error range of 5.34% to 12.21% compared to real-world experiments, indicating a high degree of accuracy. Additionally, the single-micropipette cell rotation task achieved a success rate of 69% with an average completion time of 17.13 seconds, closely matching the expert data's average time of 17.69 seconds, confirming the feasibility of the simulation system.