Autonomous navigation of intelligent microrobotic swarms in unknown environments
Autonomous navigation of intelligent microrobotic swarms in unknown environments
References
Palagi, S. & Fischer, P. Bioinspired microrobots. Nat. Rev. Mater. 3, 113–124 (2018). Article Google Scholar
Medany, M., Piglia, L., Achenbach, L., Mukkavilli, S. K. & Ahmed, D. Model-based reinforcement learning for ultrasound-driven autonomous microrobots. Nat. Mach. Intell. 3, 1076–1090 (2025). Article Google Scholar
Yang, M. et al. Swarming magnetic nanorobots bio-interfaced by heparinoid-polymer brushes for in vivo safe synergistic thrombolysis. Sci. Adv. 9, eadk7251 (2023). Article Google Scholar
Soria, E., Schiano, F. & Floreano, D. Predictive control of aerial swarms in cluttered environments. Nat. Mach. Intell. 3, 545–554 (2021). Article Google Scholar
Berlinger, F., Gauci, M. & Nagpal, R. Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm. Sci. Robot. 6, eabd8668 (2021). Article Google Scholar
Wrede, P. et al. Real-time 3D optoacoustic tracking of cell-sized magnetic microrobots circulating in the mouse brain vasculature. Sci. Adv. 8, eabm9132 (2022). Article Google Scholar
Yang, L. et al. A survey on swarm microrobotics. IEEE Trans. Robot. 38, 1531–1551 (2021). Article Google Scholar
Li, C., Kreiman, G. & Ramanathan, S. Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks. Nat. Mach. Intell. 6, 726–738 (2024). Article Google Scholar
Saggio, V. et al. Experimental quantum speed-up in reinforcement learning agents. Nature 591, 229–233 (2021). Article Google Scholar
Li, X.-K. et al. High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit. Nat. Commun. 15, 1044 (2024). Article Google Scholar
Ouyang, L. et al. Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst. 35, 27730–27744 (2022). Google Scholar
Han, R. et al. NeuPAN: direct point robot navigation with end-to-end model-based learning. IEEE Trans. Robot. 41, 2804–2824 (2025). Article Google Scholar
Nazeer, M. S., Laschi, C. & Falotico, E. RL-based adaptive controller for high precision reaching in a soft robot arm. IEEE Trans. Robot. 40, 2498–2512 (2024). Article Google Scholar
Rana, K. et al. Bayesian controller fusion: leveraging control priors in deep reinforcement learning for robotics. Int. J. Robot. Res. 42, 123–146 (2023). Article Google Scholar
Ibarz, J. et al. How to train your robot with deep reinforcement learning: lessons we have learned. Int. J. Robot. Res. 40, 698–721 (2021). Article Google Scholar
Kaufmann, E. et al. Champion-level drone racing using deep reinforcement learning. Nature 620, 982–987 (2023). Article Google Scholar
Yang, G.-Z. et al. The grand challenges of science robotics. Sci. Robot. 3, eaar7650 (2018). Article Google Scholar
Gardi, G., Ceron, S., Wang, W., Petersen, K. & Sitti, M. Microrobot collectives with reconfigurable morphologies, behaviors, and functions. Nat. Commun. 13, 2239 (2022). Article Google Scholar
Law, J. et al. Micro/nanorobotic swarms: from fundamentals to functionalities. ACS Nano 17, 12971–12999 (2023). Article Google Scholar
Yu, J., Xu, T., Lu, Z., Vong, C. I. & Zhang, L. On-demand disassembly of paramagnetic nanoparticle chains for microrobotic cargo delivery. IEEE Trans. Robot. 33, 1213–1225 (2017). Article Google Scholar
Ahmed, D. et al. Bioinspired acousto-magnetic microswarm robots with upstream motility. Nat. Mach. Intell. 3, 116–124 (2021). Article Google Scholar
Law, J. et al. Microrobotic swarms for selective embolization. Sci. Adv. 8, eabm5752 (2022). Article Google Scholar
Wang, Q. et al. Tracking and navigation of a microswarm under laser speckle contrast imaging for targeted delivery. Sci. Robot. 9, eadh1978 (2024). Article Google Scholar
Yu, E.-S. et al. Precise capture and dynamic relocation of nanoparticulate biomolecules through dielectrophoretic enhancement by vertical nanogap architectures. Nat. Commun. 11, 2804 (2020). Article Google Scholar
Ahmed, D. et al. Neutrophil-inspired propulsion in a combined acoustic and magnetic field. Nat. Commun. 8, 770 (2017). Article Google Scholar
Dong, X. & Sitti, M. Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms. Int. J. Robot. Res. 39, 617–638 (2020). Article Google Scholar
Yigit, B., Alapan, Y. & Sitti, M. Programmable collective behavior in dynamically self-assembled mobile microrobotic swarms. Adv. Sci. 6, 1801837 (2019). Article Google Scholar
An, X., Xu, Z., Fang, K. & Wang, Q. Model-free control of magnetic microrobotic swarm for on-demand pattern spreading. IEEE Robot. Autom. Lett. 9, 3187–3194 (2024). Article Google Scholar
Liu, Y. et al. Shape reconfiguration and path planning of microswarms for automatic collision avoidance. In Proc. I EEE International Conference on Real-time Computing and Robotics (RCAR) 7–12 (IEEE, 2023).
Xie, H. et al. Reconfigurable magnetic microrobot swarm: multimode transformation, locomotion, and manipulation. Sci. Robot. 4, eaav8006 (2019). Article Google Scholar
Yu, J. et al. Adaptive pattern and motion control of magnetic microrobotic swarms. IEEE Trans. Robot. 38, 1552–1570 (2022). Article Google Scholar
Hortelão, A. C. et al. Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder. Sci. Robot. 6, eabd2823 (2021). Article Google Scholar
Jiang, J. et al. Automated microrobotic manipulation using reconfigurable magnetic microswarms. IEEE Trans. Robot. 40, 3676–3694 (2024). Article Google Scholar
Wang, Q. et al. Ultrasound Doppler-guided real-time navigation of a magnetic microswarm for active endovascular delivery. Sci. Adv. 7, eabe5914 (2021). Article Google Scholar
Heuthe, V.-L., Panizon, E., Gu, H. & Bechinger, C. Counterfactual rewards promote collective transport using individually controlled swarm microrobots. Sci. Robot. 9, eado5888 (2024). Article Google Scholar
Salehizadeh, M. & Diller, E. Three-dimensional independent control of multiple magnetic microrobots via inter-agent forces. Int. J. Robot. Res. 39, 1377–1396 (2020). Article Google Scholar
Liu, Y., Zeng, G., Du, X., Fang, K. & Yu, J. Navigated locomotion and controllable splitting of a microswarm in a complex environment. In Proc. International Conference on Intelligent Robots and Systems (IROS) 1337–1342 (IEEE, 2024).
Liu, Y. et al. Automatic navigation of microswarms for dynamic obstacle avoidance. IEEE Trans. Robot. 39, 2770–2785 (2023). Article Google Scholar
Yang, L. et al. Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning. Nat. Mach. Intell. 4, 480–493 (2022). Article Google Scholar
Liu, B., Cai, Q., Yang, Z. & Wang, Z. Neural trust region/proximal policy optimization attains globally optimal policy. In Advances in Neural Information Processing Systems (NeurIPS) (Wallach, H. et al.) Vol. 32 (Curran Associates, 2019).
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS) (eds Guyon, I. et al.) Vol. 30 (Curran Associates, 2017).
Su, J. et al. RoFormer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024). Article Google Scholar
Zhang, B. & Sennrich, R. Root mean square layer normalization. In Advances in Neural Information Processing Systems (NeurIPS) (eds Wallach, H. et al.) Vol. 32 (Curran Associates, 2019).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).
Pleines, M., Pallasch, M., Zimmer, F. & Preuss, M. Memory gym: partially observable challenges to memory-based agents. In Proc. I nternational Conference on Learning Representations (ICLR) (OpenReview.net, 2023).
Parisotto, E. et al. Stabilizing transformers for reinforcement learning. In Proc. I nternational Conference on Machine Learning (ICML) (eds Daumé. H. III & Singh, A.) 7487–7498 (PMLR, 2020).
Yang, L. et al. Machine learning for micro- and nanorobots. Nat. Mach. Intell. 6, 605–618 (2024). Article Google Scholar
Wu, Z., Chen, Y., Mukasa, D., Pak, O. S. & Gao, W. Medical micro/nanorobots in complex media. Chem. Soc. Rev. 49, 8088–8112 (2020). Article Google Scholar
Patiño Padial, T., Chen, S., Hortelão, A. C., Sen, A. & Sánchez, S. Swarming intelligence in self-propelled micromotors and nanomotors. Nat. Rev. Mater. 10, 947–963 (2025).
Wang, B. et al. Endoscopy-assisted magnetic navigation of biohybrid soft microrobots with rapid endoluminal delivery and imaging. Sci. Robot. 6, eabd2813 (2021). Article MathSciNet Google Scholar
Nelson, B. J. & Pané, S. Delivering drugs with microrobots. Science 382, 1120–1122 (2023). Article Google Scholar
Du, X. et al. Active exploration and reconstruction of vascular networks using microrobot swarms. Nat. Mach. Intell. 7, 553–564 (2025). Article Google Scholar
Lin, H. et al. Ferrobotic swarms enable accessible and adaptable automated viral testing. Nature 611, 570–577 (2022). Article Google Scholar
Felfoul, O. et al. Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions. Nat. Nanotechnol. 11, 941–947 (2016). Article Google Scholar
Deng, H. et al. Monodisperse magnetic single-crystal ferrite microspheres. Angew. Chem. 117, 2842–2845 (2005). Article Google Scholar
Petousis, I., Homburg, E., Derks, R. & Dietzel, A. Transient behaviour of magnetic micro-bead chains rotating in a fluid by external fields. Lab Chip 7, 1746–1751 (2007). Article Google Scholar
Melle, S., Calderón, O. G., Rubio, M. A. & Fuller, G. G. Microstructure evolution in magnetorheological suspensions governed by Mason number. Phys. Rev. E 68, 041503 (2003). Article Google Scholar
Volkova, O., Cutillas, S. & Bossis, G. Shear banded flows and nematic-to-isotropic transition in ER and MR fluids. Phys. Rev. Lett. 82, 233 (1999). Article Google Scholar
Du, X., Yu, J., Jin, D., Chiu, P. W. Y. & Zhang, L. Independent pattern formation of nanorod and nanoparticle swarms under an oscillating field. ACS Nano 15, 4429–4439 (2021). Article Google Scholar
Cai, M. et al. Deep reinforcement learning framework-based flow rate rejection control of soft magnetic miniature robots. IEEE Trans. Cybern. 53, 7699–7711 (2022). Article Google Scholar
Xuanyu, A. et al. seuwanglab/intelligent-microrobotic-swarm-in-unknown-environments: intelligent microrobotic swarm in unknown environments. Zenodo https://doi.org/10.5281/zenodo.18153971 (2026). Download references
Source: Nature