The development goal of artificial intelligence is to enable machines to think and act like humans, solving complex problems, and more importantly, interacting effectively in a complex, dynamic, and uncertain physical world. Embodied intelligence emphasizes that intelligent agents continuously learn and evolve from perception, decision-making, and action processes. It is realized through dynamic interactions with their surroundings via physical embodiments. This approach overcomes the limitations of traditional static data-driven training models, demonstrating superior adaptability and generalization capabilities in the real world. It therefore has become a dominant way to achieve the goal of artificial intelligence. This study explores the conceptual connotations, computational frameworks, and system implementations of embodied intelligence, and, on this basis, further reviews its current development status, evolutionary trends, and challenges. In particular, the study highlights the pivotal role of generative artificial intelligence, especially large language models, multimodal large language models, and the advancing large “information ‒ physical ‒ cognitive” models, in accelerating the evolution of embodied intelligence. In the face of intensifying global competition in artificial intelligence, this study further summarizes the achievements and analyzes the risks in the development of embodied intelligence in China, and proposes key research directions and targeted policy recommendations to help China secure a leading position in the global race for embodied intelligence.