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Researchers at Leipzig University have achieved a breakthrough in microrobotics. For the first time, they have shown that tiny synthetic microswimmers can perceive their surroundings directly through their own body shape and autonomously adapt to rapidly changing fluid flows. The study, now published in Science Advances, establishes a new paradigm for autonomous microsystems whose control functions reliably in challenging environments where conventional sensors fail. This opens up new prospects for autonomous medical microrobots, for example for the targeted delivery of medication in the bloodstream.
The research team, led by Professor Frank Cichos from the Molecular Nanophotonics Group at Leipzig University’s Peter Debye Institute for Soft Matter Physics, used reinforcement learning – a machine-learning approach – to control microswimmers navigating through complex flow fields. The microscopic particles learned successful navigation strategies using algorithms, even though they had no direct sensory information about the flows opposing their movement. Because each movement of the particles already carried the signature of the surrounding flow, their bodies themselves served as sensors and thus as the data basis for the algorithm.
Professor Cichos emphasises the broader significance: “This work bridges biological inspiration and practical implementation. Motile microorganisms have evolved over millions of years to use their physical structure for navigation. We now show that machine learning can discover similar strategies in synthetic systems within experimentally feasible time frames.”
Physics becomes a learning and decision-making system for microswimmers
The researchers combine melamine particles coated with gold nanoparticles – so-called synthetic microswimmers (with a radius of around 1 micrometre) – with real-time optical control and machine-learning algorithms. The particles are propelled by asymmetric laser illumination. During the training phases, they learn to reach their target locations despite hydrodynamic disturbances caused by laser-induced flows.
“The experiments themselves were quite demanding,” says Dr Diptabrata Paul, a research associate at the Peter Debye Institute. “We had to achieve stable real-time control while simultaneously training the learning algorithm – essentially, we were teaching the microswimmers how to behave as they navigated. The particles are exposed to flows up to four times stronger than their own propulsion speed, yet they learn to navigate successfully within around 50 training episodes.”
The key insight lies in what researchers call “embodied intelligence” – the principle that physical structures and interactions with the environment can serve as computational resources for algorithms. Rather than relying on miniaturised sensors and processors, the motion dynamics of the microswimmers themselves become information processors.
“This is fundamentally different from our usual conception of robot design,” explains Paul. “Instead of trying to capture everything explicitly through sensors and then compute responses, the physical interaction between the body and its environment is used to obtain the necessary information. The learning algorithm discovers how to read and respond to this embodied information.”
Autonomous microsystems without sensors: A new paradigm
The work has significant implications for applications in which explicit sensing is impractical or impossible. “Consider the targeted delivery of medication within the human body,” suggests Dr Nico Scherf from the Max Planck Institute for Human Cognitive and Brain Sciences. “Conventional approaches rely on pre-programmed responses or external control, but physiological flows are complex and unpredictable. Microrobots that learn from their own dynamics could potentially move autonomously within the body.” The research also opens up new avenues for swarm robotics: multiple microrobots could exhibit collective embodied intelligence.
“We are really only at the beginning of exploring what becomes possible when we treat physical embodiment as a computational resource,” Paul concludes. “This work demonstrates the principle experimentally. The next challenge is to transfer these ideas to more complex environments and tasks.”
Research team
In addition to Dr Diptabrata Paul and Professor Frank Cichos from Leipzig University, the research team included Nikola Milosevic and Dr Nico Scherf from the Max Planck Institute for Human Cognitive and Brain Sciences, who contributed their expertise in machine-learning optimisation. All of the researchers are affiliated with the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig). The research was supported by the Federal Ministry for Research, Technology and Space (BMFTR) as part of the ACONITE project and by ScaDS.AI Dresden/Leipzig.
Text: Jana Bendigs / Translation: Matthew Rockey
Professor Frank Cichos
Peter Debye Institute for Soft Matter Physics
Phone: +49 341 97-32571
Email: cichos@physik.uni-leipzig.de
Original title of the publication in Science Advances:
“Physical Embodiment Enables Information Processing Beyond Explicit Flow Sensing in Active Matter”, DOI: 10.1126/sciadv.aec0783 https://www.science.org/doi/10.1126/sciadv.aec0783
https://scads.ai Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig)
Dr Diptabrata Paul adjusting the experimental setup in which machine learning and microswimmers are ...
Source: Frank Cichos
Copyright: Leipzig University
The research team with Dr Diptabrata Paul, Dr Nico Scherf, Professor Frank Cichos and Nikola Milosev ...
Source: Leipzig University
Copyright: Leipzig University
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Dr Diptabrata Paul adjusting the experimental setup in which machine learning and microswimmers are ...
Source: Frank Cichos
Copyright: Leipzig University
The research team with Dr Diptabrata Paul, Dr Nico Scherf, Professor Frank Cichos and Nikola Milosev ...
Source: Leipzig University
Copyright: Leipzig University
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