Reinforcement learning of biomimetic navigation: a model problem for sperm chemotaxis.

Motile biological cells can respond to local environmental cues and exhibit various navigation strategies to search for specific targets. These navigation strategies usually involve tuning of key biophysical parameters of the cells, such that the cells can modulate their trajectories to move in response to the detected signals. Here we introduce a reinforcement learning approach to modulate key biophysical parameters and realize navigation strategies reminiscent to those developed by biological cells. We present this approach using sperm chemotaxis toward an egg as a paradigm. By modulating the trajectory curvature of a sperm cell model, the navigation strategies informed by reinforcement learning are capable to resemble sperm chemotaxis observed in experiments. This approach provides an alternative method to capture biologically relevant navigation strategies, which may inform the necessary parameter modulations required for obtaining specific navigation strategies and guide the design of biomimetic micro-robotics.

Reconfigurability-Encoded Hierarchical Rectifiers for Versatile 3D Liquid Manipulation.

Manipulating small-volume liquids is crucial in natural processes and industrial applications. However, most liquid manipulation technologies involve complex energy inputs or non-adjustable wetting gradient surfaces. Here, a simple and adjustable 3D liquid manipulation paradigm is reported to control liquid behaviors by coupling liquid-air-solid interfacial energy with programmable magnetic fields. This paradigm centers around a hierarchical rectifier with magnetized microratchets, using Laplace pressure asymmetry to enable multimodal directional steering of various surface tension liquids (23-72 mN m-1). The scale-dependent effect in microratchet design shows its superiority in handling small-volume liquids across three orders of magnitude (100-103 µL). Under programmed magnetic fields, the rectifier can reconfigure its morphology to harness interfacial energy to exhibit richer liquid behaviors without dynamic real-time control. Reconfigured rectifiers show improved rectification performance in the inertia-dominant fluid regime, i.e., a remarkable 2000-fold increase in the critical Weber number for pure ethanol. Moreover, the rectifier's switchable reconfigurations offer flexible control over liquid transport directions and spatiotemporally controllable 3D liquid manipulation reminiscent of inchworm motions. This scalable liquid manipulation paradigm promotes versatile engineering and biochemistry applications, e.g., portable liquid purity testing (screening resolution <1 mN m-1), logical open-channel microfluidics, and automated chemical reaction platforms.

Propulsion of a three-sphere microrobot in a porous medium.

Microorganisms and synthetic microswimmers often encounter complex environments consisting of networks of obstacles embedded into viscous fluids. Such settings include biological media, such as mucus with filamentous networks, as well as environmental scenarios, including wet soil and aquifers. A fundamental question in studying their locomotion is how the impermeability of these porous media impacts their propulsion performance compared with the case of that in a purely viscous fluid. Previous studies showed that the additional resistance due to the embedded obstacles leads to an enhanced propulsion of different types of swimmers, including undulatory swimmers, helical swimmers, and squirmers. In this paper, we employ a canonical three-sphere swimmer model to probe the impact of propulsion in porous media. The Brinkman equation is utilized to model a sparse network of stationary obstacles embedded into an incompressible Newtonian liquid. We present both a far-field theory and numerical simulations to characterize the propulsion performance of the swimmer in such porous media. In contrast to enhanced propulsion observed in other swimmer models, our results reveal that both the propulsion speed and efficiency of the three-sphere swimmer are largely reduced by the impermeability of the porous medium. We attribute the substantial reduction in propulsion performance to the screened hydrodynamic interactions among the spheres due to the more rapid spatial decays of flows in Brinkman media. These results highlight how enhanced or hindered propulsion in porous media is largely dependent on individual propulsion mechanisms. The specific example and physical insights provided here may guide the design of synthetic microswimmers for effective locomotion in porous media in their potential biological and environmental applications.

Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination.

Biological microswimmers can coordinate their motions to exploit their fluid environment-and each other-to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers. Here we probe the emergence of such cooperative behaviors among artificial microswimmers endowed with artificial intelligence. We present the first use of a deep reinforcement learning approach to empower the cooperative locomotion of a pair of reconfigurable microswimmers. The AI-advised cooperative policy comprises two stages: an approach stage where the swimmers get in close proximity to fully exploit hydrodynamic interactions, followed a synchronization stage where the swimmers synchronize their locomotory gaits to maximize their overall net propulsion. The synchronized motions allow the swimmer pair to move together coherently with an enhanced locomotion performance unattainable by a single swimmer alone. Our work constitutes a first step toward uncovering intriguing cooperative behaviors of smart artificial microswimmers, demonstrating the vast potential of reinforcement learning towards intelligent autonomous manipulations of multiple microswimmers for their future biomedical and environmental applications.