Abstract
Microrobotic systems offer significant potential for precision medicine by enabling minimally invasive interventions in complex fluidic environments. However, effective operation in these settings requires actuators capable of more than simple linear or rotational motion, often necessitating programmable changes in both direction and shape. This remains a major challenge due to fundamental constraints in the design and control of microscale actuators, particularly in acoustic systems. Here, we introduce engineered cilia for hybrid operations microrobots, a class of acoustic microrobots that use geometry-tuned cilia and resonance-induced forces to execute complex motions such as bidirectional bending, controllable rotation, and adaptive morphing. The microrobots design is driven by a self-augmenting machine learning framework integrated with finite element analysis, enabling rapid prediction and optimization of geometry-resonance relationships across design space. This approach achieves >10⁵-fold reduction in prediction time and over 20-fold in memory savings, while maintaining >90% accuracy in peak amplitude and >98% in resonance frequency. Compliant mechanism strategies further expand the mechanical versatility of the microrobots, enabling programmable shape transformations tailored to specific tasks. These advances establish acoustic-driven microrobots as a scalable and efficient platform for intelligent microrobotic actuation in biomedical and microfluidic applications.
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Data availability
The data generated in this study have been deposited in Figshare. This study involves a large number of finite element analysis simulations across a wide parameter space. Due to file size limitations, the deposited dataset includes representative finite element simulation files and all processed data used to generate the figures and Supplementary Figs. Additional simulation files can be generated following the methods described in the manuscript.
Code availability
The code used in this study has been deposited in Code Ocean and is available at https://codeocean.com/capsule/2924489/tree.
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Acknowledgements
We acknowledge support from the National Institutes of Health (R01GM141055 (T.J.H.), R01GM145960 (T.J.H.), and R01GM144417 (T.J.H.)), National Science Foundation (CMMI-2104295 (T.J.H.)), and National Science Foundation Graduate Research Fellowship Program (2139754 (J.R.)).
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Y.L. (Yun) led the project conceptualization, methodology development, and manuscript drafting. Y.L. (Yujing) fabricated micro devices and tested the materials modulus. Y.L. (Yun), X.X., and Q.W. performed the simulations. Y.L. (Yun) also conducted the machine learning model development. K.Y. and K.J. contributed to microscopy imaging. M. L. and R.Z. assisted with schematic illustrations. S.Z. acquired fluorescent images. Y.C. supported the methodological design and provided technical input. S.L. fabricated the microchannels for device fabrication. T.J.H. supervised the research. J.R., T.N., and T.J.H. reviewed and edited the manuscript. All authors provided feedback and contributed to the final version of the manuscript. We used ChatGPT (OpenAI) to help improve the clarity and readability of part of the manuscript after completing the initial draft and to polish the code.
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T.J.H. has co-founded a start-up company, Ascent Bio-Nano Technologies Inc., to commercialize technologies involving acoustofluidics and acoustic tweezers. All other authors declare no competing interests.
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Ling, Y., Lu, Y., Rich, J. et al. Machine learning–driven design of engineered cilia enables hybrid operations in acoustic microrobots. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70048-4
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DOI: https://doi.org/10.1038/s41467-026-70048-4
