Core Insights - The current manufacturing automation faces a fundamental contradiction between the demand for personalized, flexible production and the traditional structured environment of industrial robots [1] - The shift from model-based programming to data-driven learning in robotics is being driven by advancements in large model technologies, particularly those based on the Transformer architecture [1][4] Group 1: Challenges and Opportunities - The paradox of efficiency and versatility in robotics indicates that while general-purpose robots may not be as efficient in specific tasks compared to specialized robots, the industry is focused on resolving this issue [2] - The core breakthrough in embodied intelligence is enabling robots to understand and plan tasks, moving beyond simple programmed actions to a complex architecture of task understanding, action planning, and execution [4] Group 2: Data and Technological Framework - The "data pyramid" theory proposed by the company emphasizes the importance of various data types, ranging from vast internet data at the base to high-value real-world data at the top, with increasing quality and cost as one moves up the pyramid [5] - The "one brain, multiple small brains" model is a practical approach where a foundational model is pre-trained on large datasets, while specialized models are fine-tuned with real-world data to optimize actions in specific scenarios [7] Group 3: Industry Collaboration and Standards - The transition to embodied intelligence is expected to follow a gradual path from structured to semi-structured and eventually to fully general scenarios, necessitating collaboration across the industry [7] - The company's "platform + track" strategy aims to empower ecosystem partners through foundational model capabilities, while also focusing on specific sectors like industrial manufacturing, logistics, and retail [8]