具身智能无共识，就是最好的共识

Core Insights - The complexity of embodied intelligence emphasizes that it is sculpted through numerous trials, conflicts, and harmonizations rather than a single correct path [1][3] - The lack of consensus in the industry is seen as an opportunity for innovation and flexibility, allowing diverse teams to explore different technical routes without being constrained by established standards [3][4] Industry Perspective - The absence of consensus breaks the monopoly of a single technical route, preventing the industry from falling into "path dependency" traps [3] - This state of "no consensus" provides opportunities for small and medium enterprises, startups, and cross-industry players to enter the market without adhering to existing technical standards [3] - The rapid iteration of technology in the interdisciplinary field of embodied intelligence suggests that premature consensus could hinder breakthroughs [3] Signals for Future Development - Signal 1: World Models Are Not Yet Sufficient The current world models, while valuable for prediction, cannot serve as a universal solution for embodied intelligence due to their reliance on human behavior data, which is not directly applicable to robotic operations [4][5] - Signal 2: Need for Specialized Models There is a growing consensus among companies to develop specialized models for embodied intelligence, focusing on actions rather than language, to better adapt to the physical world [6][7] - Signal 3: Innovation from the Ground Up The applicability of the Transformer architecture in embodied intelligence is being questioned, with suggestions to explore new architectures that prioritize direct interaction between vision and action [7][8] - Signal 4: Data as Fuel Data is recognized as essential for embodied intelligence, but there is no unified approach on the types of data to use, leading to a strategy of multi-source integration based on specific task requirements [9][10] - Signal 5: Growing Demand for Data As embodied intelligence penetrates more complex scenarios, the demand for data is increasing in terms of quantity, quality, and variety, necessitating a more comprehensive approach to data collection [11][13][14]