Over billions of years of evolution, motile organisms have developed complex strategies to survive and thrive. These strategies integrate three components: sensors, actuators, and information processing. In the last two decades, active-matter research has tried to replicate the evolutionary success of microorganisms in artificial systems. Researchers have replicated the actuators by developing artificial active particles that extract energy from their environment to perform mechanical work and, to a lesser extent, the sensors, by making these active particles adjust their motion properties to physical cues. However, these artificial particles are still largely incapable of autonomous information processing, which is limiting the scientific insight and technological applications of active matter. The main challenges are: 1. Make active particles capable of autonomous information processing. 2. Optimize the behavioral strategies of individual active particles. 3. Optimize the interactions between active particles. Drawing inspiration from Nature, this project will take the next steps in the evolution of artificial active matter systems by endowing them with embodied intelligence and autonomous information processing abilities. Specifically, it will: 1. Realize microscopic active particles with embodied intelligence (microbots). 2. Use embodied intelligence to achieve optimal behaviors for the microbots. 3. Use embodied intelligence to engineer interactions between microbots. I will achieve this by combining my background in mesoscopic physics and microfabrication with machine learning, a new research direction that offers radically different and complementary opportunities. This project will provide scientific insight into far-from-equilibrium physics and lay the foundations for ground-breaking applications empowered by microbots that are able to autonomously sense and react to their microscopic environment.
Fields of science
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