Programable Acoustic Single‐Cell Manipulation With Model‐Free Machine Learning

Precise, noninvasive manipulation of individual living cells remains a central challenge in biomedical science, with far‐reaching implications for single‐cell analysis, tissue engineering, and the study of cell–cell interactions. Here, we report the first demonstration of single‐cell control using bulk acoustic standing‐wave acoustofluidics with closed‐loop feedback. We introduce VeLO (Vector‐based Local Optimization), a model‐free, reinforcement learning–inspired algorithm that enables programable two‐dimensional manipulation of individual cells using a single piezoelectric transducer. Without prior calibration or physical modeling, VeLO learns system dynamics online from acoustically induced cell displacements and automatically adapts to nonlinear, time‐varying conditions. We achieve robust control across multiple cell types (DU‐145, Jurkat, K‐562) and independent manipulation of multiple cells, including controlled cell–cell contact. By combining simplicity of hardware with autonomous, adaptive control, this approach establishes multimodal acoustofluidics as a versatile tool for label‐free, high‐precision single‐cell manipulation.