Group 1 - The core argument emphasizes the necessity of "good data" for the successful implementation of physical AI, which must meet three key standards: physical authenticity, semantic comprehensibility, and scene generalization [2][4][8] - Physical authenticity is described as the "skeleton" of data, essential for accurately representing the physical world's rules, including geometric structures and material properties [3][4] - Semantic comprehensibility is highlighted as the "soul" of data, enabling intelligent agents to understand and execute commands in the physical world, requiring a deep connection between visual recognition and semantic understanding [3][4] Group 2 - Scene generalization is identified as crucial for breaking "data silos," allowing intelligent agents to adapt to various physical environments by extracting universal rules from limited training scenarios [4][8] - The rise of embodied intelligence is discussed as a transformative approach to AI, focusing on active interaction with the physical world to enhance learning and intelligence [5][6] - Challenges faced by embodied intelligence include the difficulty of simulating real-world interactions and the discrepancies between simulated and actual environments, which can hinder effective data acquisition and processing [6][7] Group 3 - The development of embodied intelligence is seen as reshaping the interaction logic between AI and the physical world, requiring data that captures both static and dynamic characteristics of physical entities [7][8] - The relationship between "good data" standards and the evolution of embodied intelligence is reciprocal, with "good data" providing a foundation for physical AI and embodied intelligence enriching the concept of "good data" [8]