In‐Device Topological Encoding for Intelligent Multimodal Interactions

Abstract For accurate intent recognition and differential decision making in dynamic interactive scenarios, a scenario‐adaptive interactive interface with touch features and external factor‐perceiving capability is essential. However, existing interactive interfaces based on discrete array structures are constrained by excessive data redundancy, sensing‐computing separation, and external factor insensitivity. In this study, an intelligent interactive interface for multimodal synergistic cognition (MSC), which adopts a signal linear partition strategy with spatiotemporal encoding, and innovatively enables the addressable perception and fusion of multimodal features within only one channel. Designing a heterogeneous transconductance structure, the MSC interactive interface mimics the multidimensional amplitude weighting and event‐driven logic of the biological stimulation gating mechanism, achieving multipoint and wide‐range (0.8–250 kPa) tactile decoding and external‐factor‐based intent correction in complex scenarios. Owing to cuttability and topological reconfiguration, the MSC interactive interface promotes the form generalization of interactive entities and facilitates intelligent learning (accuracy > 99%) of unstructured features. The demonstration of touch‐strain‐driven robotic arm control highlights the importance of touch cognitive alignment with external factors in non‐predefined scenes. This study promotes the transition of intelligent machines from passive response to differentiable cognition with external‐factor enhancement, providing a foundational approach to a triadic cognitive framework integrating touch, machine, and external factors.