Bioinspired Cross‐Modal Self‐Adaptive Machine Intelligence for Event‐Driven and Ultrahigh‐Precision Underwater Grasping

Abstract

ABSTRACT Embodied intelligent agents, which represent the future of robotics, demand precise perception and real‐time decision‐making capabilities to achieve natural environmental interactions. However current systems face inherent limitations in unimodal sensing and cross‐modal coordination, which hinder their performance in dynamic contact‐rich operations. Herein, we present a fabric‐based event‐driven tactile interface that features an innovative woven structure with cross‐fiber electrodes. It achieves breakthroughs in sensitivity (246.3 kPa −1 ), pressure detection (>450 kPa), and waterproof robustness. This interface enables millisecond‐level pressure/slip dual‐mode feedback for self‐adaptive grasping, thereby improving the dexterous manipulation of fragile or slippery objects. For underwater scenarios, a bio‐inspired visual–tactile fusion (VTF) architecture leverages tactile perception to compensate for visual limitations, demonstrating a high accuracy of 97.7% in complex tasks, including underwater transparent object manipulation and recognition of similar objects. Event‐driven tactile feedback is merged with visual semantics for decision‐level optimization, thereby enhancing the autonomy and adaptation of humanoid machine intelligence. It creates an innovative closed‐loop cross‐modal perception–decision system that builds a direct link between environmental interaction and autonomous decision‐making for intelligent agent development in open‐world scenarios. The superior performance of the VTF architecture dynamic interaction tasks represents a crucial step toward robotic systems with advanced intelligence.