Physical AI: Bridging Digital and Real Worlds Physical AI is gaining traction by integrating intelligent agents into real-world environments, enhancing tasks through environmental interaction and motion control. Industry experts foresee significant commercialization potential in various sectors. Par Yu Sinan, People's Daily Artificial intelligence continues to evolve remarkably—from image recognition and text generation to video creation—demonstrating increasingly sophisticated capabilities. As these digital capabilities mature, the technology sector is shifting focus toward integrating AI into physical environments. This emerging concept, known as physical AI, is gaining significant traction within the industry. Physical AI represents intelligent agents capable of perceiving physical environments and performing human-like actions beyond digital interfaces. Ma Xiaojian, head of the joint laboratory between the Beijing Institute for General Artificial Intelligence and Delta Intelligence, noted that physical AI has three defining features: its capabilities are built on real-world physical interaction data, it incorporates an understanding of the physical world, and it can be deployed in real-world physical entities. Where generative AI excels in content creation and data analysis, physical AI specializes in environmental interaction and motion control tasks. "While representing different AI dimensions, these domains demonstrate growing convergence," Ma noted. Generative AI's capabilities—including language interpretation, scenario modeling, and automated coding—enhance physical AI's task execution and environmental navigation. Over the past few years, the tech industry has advanced physical AI from core algorithms to ontology engineering through multiple approaches. Ma said that there are three main technical pathways currently used to implement physical AI. The first is the "pre-training and post-training" approach, in which models undergo large-scale pre-training on internet videos, first-person videos, and cross-robot manipulation data before being further refined through teleoperation data, reinforcement learning, or real-world fine-tuning. The second is the "real-simulation-real" approach, which reconstructs real-world geometry, materials, and dynamics into high-fidelity simulation environments, enabling robots to learn through