Machine learning (ML) has revolutionized robotics by enhancing perception, adaptability, decision-making and more, enabling robots to work in complex scenarios beyond the capabilities of traditional approaches. However, the downsizing of robots to micro- and nanoscales introduces new challenges. For example, complexities in the actuation and locomotion of micro- and nanorobots defy traditional modelling methods, while control and navigation are complicated by strong environmental disruptions, and tracking in vivo encounters substantial noise interference. Recently, ML has also been shown to offer a promising avenue to tackle these complexities. Here we discuss how ML advances many crucial aspects of micro- and nanorobots, that is, in their design, actuation, locomotion, planning, tracking and navigation. Any application that can benefit from these fundamental advancements will be a potential beneficiary of this field, including micromanipulation, targeted delivery and therapy, bio-sensing, diagnosis and so on. This Review aims to provide an accessible and comprehensive survey for readers to quickly appreciate recent exciting accomplishments in ML for micro- and nanorobots. We also discuss potential issues and prospects of this burgeoning research direction. We hope this Review can foster interdisciplinary collaborations across robotics, computer science, material science and allied disciplines, to develop ML techniques that surmount fundamental challenges and further expand the application horizons of micro- and nanorobotics in biomedicine.

机器学习 (ML) 通过增强感知、适应性、决策等能力，彻底改变了机器人技术，使机器人能够在超出传统方法能力的复杂场景中工作。然而，机器人尺寸缩小到微米和纳米尺度带来了新的挑战。例如，微型和纳米机器人的驱动和运动的复杂性违背了传统的建模方法，而控制和导航由于强烈的环境干扰而变得复杂，并且体内跟踪遇到了大量的噪声干扰。最近，机器学习也被证明为解决这些复杂性提供了一种有前途的途径。在这里，我们讨论机器学习如何推进微型和纳米机器人的许多关键方面，即设计、驱动、运动、规划、跟踪和导航。任何能够受益于这些基本进步的应用都将成为该领域的潜在受益者，包括显微操作、靶向递送和治疗、生物传感、诊断等。本综述旨在为读者提供一份易于理解且全面的调查，以快速了解微型和纳米机器人机器学习领域最近取得的令人兴奋的成就。我们还讨论了这个新兴研究方向的潜在问题和前景。我们希望这篇综述能够促进机器人学、计算机科学、材料科学和相关学科之间的跨学科合作，开发克服基本挑战的机器学习技术，并进一步扩大微米和纳米机器人在生物医学中的应用范围。