Enabling microrobotic chemotaxis via reset-free hierarchical reinforcement learning
We demonstrate chemotactic navigation of a multi-link articulated microrobot using two-level hierarchical reinforcement learning (RL). The lower-level RL allows the robot—featuring either a chain or ring topology—to acquire topology-specific swimming gaits: wave propagation characteristic of flagella or body oscillation akin to an ameboid. Such flagellar and ameboid microswimmers, further enabled by the higher-level RL, accomplish chemotactic navigation in prototypical biologically-relevant scenarios that feature conflicting chemoattractants, pursuing a swimming bacterial mimic, steering in vortical flows, and squeezing through tight constrictions.
T. Xiong, Z. Liu, C. J. Ong and L. Zhu*, 2024, arXiv: 2408.07346
Learn to swim like a beating flagellum: Transverse wave
Learn to swim like an oscillating ameboid: Longitudinal wave
We employ large-scale, agent-resolved simulations to demonstrate that modulating the activity of a wet phoretic medium alone can govern its solid-liquid-gas phase transitions and, subsequently, laminar-turbulent transitions in fluid phases, thereby shaping its emergent pattern. These two progressively emerging transitions, hitherto unreported, bring us closer to perceiving the parallels between active matter and traditional matter. Our work reproduces and reconciles seemingly conflicting experimental observations on chemically active systems, presenting a unified landscape of phoretic collective dynamics.
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Q. Yang^, M. Jiang^, F. Picano and L. Zhu*, Nat. Commun., 15, 2874, 2024​, selectively featured in Editor's Highlights. ^: equal contribution.
Active Wigner crystal
Liquid phase
Gas phase
Active turbulence
Data-driven intelligent manipulation of micro-scale particles
We present a data-driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. It replaces the difficult-to-derive model by a generally trainable artificial neural network to describe the particle kinematics, and subsequently identifies the optimal operations to manipulate particles. We demonstrate various manipulations, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles.
W. Fang^, T. Xiong^, O. S. Pak and L. Zhu*, Adv. Sci., 2205382, 2022​. ​^: equal contribution.
Mimicking biological self-oscillations via an elasto-electro-hydrodynamic instability
Flagella and cilia beat or wiggle in a self-oscillatory fashion (not periodically actuated). Instead, prior studies commonly required a periodic power source to generate the forced oscillation of artificial filaments. Here, we engineer self-oscillation of artificial structures using an elasto-electro-hydrodynamic instability. We show numerically that applying a steady uniform electric field can produce the self-oscillatory locomotion of a microrobot composed of a dielectric particle and an elastic filament (see the video below). Linear stability analysis is performed to delineate this instability and the underlying physics. Experiments are then performed to realize this concept.
The strategy was conceived numerically in
​L. Zhu and H.A. Stone, Phys. Rev.Fluids, 4, 061701, Rapid Communications, 2019,
analyzed theoretically in
and realized experimentally in
E. Han, L. Zhu, J.W.Shaevitz and H. A. Stone, Proc. Natl. Acad. Sci. U.S.A., 118 (29), 2021.
A particle-encapsulating droplet in a creeping shear flow: instability and bifurcations
To understand the behavior of composite fluid particles such as nucleated cells and double emulsions in flow, we study a finite-size particle encapsulated in a droplet under creeping shear flow as a model system. In addition to its concentric particle-droplet configuration, we numerically explore other eccentric and time-periodic equilibrium solutions, which emerge spontaneously via supercritical pitchfork and Hopf bifurcations.
L. Zhu* and F.Gallaire, Phys.Rev.Lett., 119, 064502, 2017.