TY - JOUR
T1 - Machine learning for micro- and nanorobots
AU - Yang, Lidong
AU - Jiang, Jialin
AU - Ji, Fengtong
AU - Li, Yangmin
AU - Yung, Kai Leung
AU - Ferreira, Antoine
AU - Li, Zhang
N1 - Publisher Copyright:
© Springer Nature Limited 2024.
PY - 2024/6/27
Y1 - 2024/6/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85196982466&partnerID=8YFLogxK
U2 - 10.1038/s42256-024-00859-x
DO - 10.1038/s42256-024-00859-x
M3 - Review article
SN - 2522-5839
VL - 6
SP - 605
EP - 618
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
IS - 6
ER -