Jiachen Han, Zi Wang, T Wang, Jialin Meng
ABSTRACT In recent years, rapidly advancing artificial intelligence has injected new momentum into advanced robotics, enabling its application across numerous domains. However, traditional computing architectures struggle to support robots in achieving complex environmental perception and autonomous decision‐making capabilities. Neuromorphic electronic devices, which mimic biological nervous systems, offer advantages such as low power consumption, parallel computing, adaptive learning, and event‐driven processing. These attributes show considerable promise in autonomous robot perception, and human–machine interaction. This review presents a comprehensive analysis of the material systems used in neuromorphic devices. Importantly, performance metrics such as memory characteristics and plasticity are employed to evaluate device performance. Building on this hardware foundation, the article further explores the software and hardware implementations of neuromorphic computing based on such devices, with a focus on recent applications in the field of embodied intelligent robotics. Finally, current technological challenges are discussed, and future research directions are proposed, with the aim of paving the way for next‐generation intelligent robotic systems.