Group 1: Industry Overview - The field of embodied intelligence is gaining momentum, with major tech companies globally investing heavily, resulting in billions in financing [1] - The World Robot Conference (WRC 2025) in Beijing showcased over 200 robotics companies demonstrating their capabilities, including various applications of embodied intelligence [1] Group 2: Understanding Embodied Intelligence - Embodied intelligence integrates AI into physical robots, enabling them to perceive and interact with the environment similarly to humans, learning through sensory feedback [2][4] - Non-embodied AI, or Internet AI, operates without physical interaction and relies on data input, contrasting with the experiential learning of embodied intelligence [2] Group 3: Data Challenges - The industry faces significant challenges in data acquisition, primarily due to high costs and the difficulty in generating large-scale datasets [5][7] - The need for high-quality, diverse data is critical, as embodied intelligence applications require extensive environmental data for effective operation [7][8] Group 4: Data Isolation and Solutions - The existence of "data silos" hinders data sharing between companies, leading to inefficiencies and wasted resources in the industry [8] - The reliance on synthetic data is increasing, with a significant portion of data in the embodied intelligence field being generated through simulation rather than real-world collection [9][10] Group 5: Future Prospects - The commercial viability of embodied intelligence robots is still in development, with mass production expected to take several more years due to high training and production costs [12] - The industry anticipates a future where embodied intelligence robots become commonplace in everyday life, although this transition may take time [12]