Embodied intelligence, or embodied AI, refers to artificial systems whose cognitive processes emerge from continuous sensorimotor interactions in real-world environments. This chapter explores its foundational theories, examines implementations across multiple domains, and evaluates both the technical and ethical considerations. A triadic human–AI collaboration framework illustrates how embodied AI agents can provide situation awareness, share control, or override capabilities to enhance safety and performance in domains ranging from automated driving to robotics-assisted surgery. Additionally, human-centered design principles are introduced, emphasizing transparency, explainability, lifecycle maintenance, and human controllability. Interaction modalities, including multimodal sensing, social robotics, and affective cues, further demonstrate how embodiment can advance human–AI collaborations. Discussions on integrating large language models (LLMs) into embodied AI are also included. Across applications such as healthcare, automated driving, education, and manufacturing, embodied systems have demonstrated how physical presence, context awareness, and adaptive learning can produce more responsive and user-friendly technology.