Physics-constrained learning framework for trustworthy microrobot navigation autonomy

Machine learning has become an emerging paradigm for microrobotics, enabling autonomous micro-/nanorobot navigation in complex and highly disturbed environments without requirements of precise models. However, the state-of-the-art learning-based methods adopt "black-box" neural networks (NNs), which raises critical concerns about the trustworthiness of the generated navigation policies, especially for biomedical scenarios. Motivated to address this issue, we propose a physics-constrained learning framework that embeds deterministic physical laws into network architectures to achieve trustworthy microrobot navigation. Instead of learning from scratch, we explicitly encode the monotonic relationships of kinematics and safety rules into NNs. These constraints serve as structural inductive biases, theoretically guaranteeing that navigation policies operate strictly within the trustworthy action domains. Experiments demonstrate that microrobots can autonomously reach random targets without collision with obstacles during long-time and long-distance navigation, proving our framework's reliability owing to its explainable nature. This work can bridge the gap between data-driven performance and rigorous safety requirements, constituting a meaningful step toward trustworthy artificial intelligence-empowered microrobotics.

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Publication Details

Journal
Science Advances
Published
2026-07-24
DOI
https://doi.org/10.1126/sciadv.aef7326
Primary Topic
Micro and Nano Robotics
Type
article
Field-Weighted Citation Impact
0.00

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article

Physics-constrained learning framework for trustworthy microrobot navigation autonomy

Lidong Yang, Chunhui Yuan, Jiachi Zhao, Antoine Ferreira et al.
Science Advances
Micro and Nano Robotics
article

Physics-constrained learning framework for trustworthy microrobot navigation autonomy

Lidong Yang, Chunhui Yuan, Jiachi Zhao, Antoine Ferreira, Kai Leung Yung, Qing Chen, Aoji Zhu, Xiang Li, Li Zhang, Yue Wang, Yamei Li, Y W Sun, Danjing Shi
article en

Abstract

Machine learning has become an emerging paradigm for microrobotics, enabling autonomous micro-/nanorobot navigation in complex and highly disturbed environments without requirements of precise models. However, the state-of-the-art learning-based methods adopt "black-box" neural networks (NNs), which raises critical concerns about the trustworthiness of the generated navigation policies, especially for biomedical scenarios. Motivated to address this issue, we propose a physics-constrained learning framework that embeds deterministic physical laws into network architectures to achieve trustworthy microrobot navigation. Instead of learning from scratch, we explicitly encode the monotonic relationships of kinematics and safety rules into NNs. These constraints serve as structural inductive biases, theoretically guaranteeing that navigation policies operate strictly within the trustworthy action domains. Experiments demonstrate that microrobots can autonomously reach random targets without collision with obstacles during long-time and long-distance navigation, proving our framework's reliability owing to its explainable nature. This work can bridge the gap between data-driven performance and rigorous safety requirements, constituting a meaningful step toward trustworthy artificial intelligence-empowered microrobotics.

Science AdvancesVol. 12(30)
Hong Kong Polytechnic University (HK), Shenzhen Institute of Information Technology (CN), Chinese University of Hong Kong (HK), Shenzhen Polytechnic University (CN), Peking University (CN), Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (FR), City University of Hong Kong, Shenzhen Research Institute (CN), Services déconcentrés d'appui à la recherche Val de Loire (FR), Peking University Third Hospital (CN), Institut National des Sciences Appliquées Centre Val de Loire (FR), University of Hong Kong (HK), Tsinghua University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 12%
Micro and Nano Robotics
DOIOpen Access PDFOpenAlex
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