Adaptive transparent cloaking tunnel enabled by Meta-Reinforcement-Learning Metasurfaces

Abstract Conventional electromagnetic cloaking paradigms predominantly necessitate the encasing of static objects within predefined topological enclosures, fundamentally restricting invisibility to fixed, closed geometries. Realizing dynamic, adaptive concealment for arbitrary moving targets within an open, boundary-free aperture remains a formidable challenge. Here, we report a meta-reinforcement-learning metasurface (Meta 2 Surface) that enables the first experimental demonstration of a "transparent cloaking tunnel" (TCT)—an open corridor permitting the undetected passage of diverse objects. Distinguished from traditional adaptive cloak, the Meta 2 Surface employs a sensor-in-the-loop meta-policy governed by a task-adaptive hypernetwork. This architecture fuses real-time sensing with historical interaction trajectories to instantly synthesize impedance strategies that actively nullify object-dependent scattering with millisecond-scale latency. Comprehensive full-wave simulations and microwave experiments confirm robust, high-fidelity cloaking of diverse dynamic targets—varying in shape, size, material, and trajectory—even under abrupt object substitution. By transitioning invisibility from static encapsulation to a dynamic, open architecture, this work establishes a new paradigm for fusing artificial intelligence with reconfigurable metasurfaces to achieve cognitive, large-scale electromagnetic wave control.