继宇树后，唯一获得三家大厂押注的自变量：具身模型不是把DeepSeek塞进机器人

Core Viewpoint - The article discusses the evolution of embodied intelligence, emphasizing that the next battleground will be the "brain" of robots, which is crucial for their autonomous operation in the physical world [1][4]. Group 1: Investment and Development - The company Zivariable has recently raised $1 billion in funding from ByteDance and Sequoia, indicating strong investor interest in their approach to robotic intelligence [1]. - Zivariable's focus is on developing a foundational model for physical intelligence that operates independently of existing AI models, aiming for a paradigm shift in how robots interact with the physical world [7][12]. Group 2: Challenges in Embodied Intelligence - The complexity of physical tasks requires robots to have a brain supported by a physical world foundational model, which is distinct from merely applying existing AI models [1][4]. - Current AI models struggle with understanding subtle physical differences that only become apparent through real-world interaction, highlighting the need for a model that can process long sequences of actions and understand causality over time [6][7]. Group 3: Model Development Approach - Zivariable advocates for an end-to-end architecture that allows for a holistic understanding of physical interactions, contrasting with the modular approach that often leads to a loss of critical details [9][10]. - The company emphasizes the importance of a general-purpose model that can learn the common structures of the physical world, similar to how language models have evolved [11]. Group 4: Unique Characteristics of Zivariable - Zivariable is committed to self-research, particularly in foundational models, believing that the next phase of competition in embodied intelligence will revolve around the ability to construct data loops and evolve models [15][16]. - The company has developed two core models, WALL-A and WALL-OSS, which integrate various aspects of embodied intelligence and have been successfully deployed in real-world scenarios [16][13]. Group 5: The Path Forward - The construction of a physical world foundational model is likened to retracing the developmental path of human infants, as it involves learning complex physical interactions that are not easily articulated [22]. - Zivariable's journey in this domain is characterized as long and challenging but ultimately rewarding, as they aim to redefine the capabilities of robots in the physical world [23].