Introduction

Over the past decade, artificial intelligence has profoundly transformed our world. From revolutionizing search engines and translation services to powering sophisticated recommendation algorithms and facial recognition systems, AI has evolved from a niche academic pursuit into an indispensable part of our daily lives. This era has been defined by the rise of "brain-like" AI—systems that can process information, understand language, and recognize patterns within the digital realm. However, this is only the first chapter of the AI story.

The next great leap in intelligence will not just be about thinking, but about acting and interacting in the physical world. This is the dawn of Embodied Intelligence. Technologies such as autonomous vehicles, advanced robotics, and immersive virtual reality systems are no longer the stuff of science fiction. They are becoming a reality, and their development marks a fundamental shift from AI that simply processes information to AI that navigates, perceives, and operates within our three-dimensional world.

Yet, a critical and largely unaddressed challenge lies at the heart of this revolution: a severe and fundamental scarcity of high-quality, real-world, Embodied AI data. The AI models that have dominated the digital space—like Large Language Models (LLMs)—have thrived on the vast, publicly available corpora of text and images scraped from the internet. In contrast, the data required for embodied AI is far more difficult to acquire. This data must come from millions of real everyday environments contributed directly by users, not only from specialized robotics devices. Both user-contributed perception data and large-scale teleoperated robot demonstrations are essential parts of this future data economy. It demands an unprecedented volume of physical-world training data, captured and contributed from thousands of decentralized, real household environments.

This data scarcity is the single greatest bottleneck holding back the embodied AI revolution. Consider the field of robotics. Embodied intelligence begins with perception. Tasks such as floor understanding, surface condition recognition, and cleanliness detection require large amounts of real household visual data before any robot can act safely or intelligently. Vision-Language-Action (VLA) models, which enable robots to connect what they see with what they understand and how they act, are the future of the industry. These models require massive, multimodal datasets to train. However, as many robotics experts have highlighted, collecting this data is currently a costly, complex, and inefficient process, often expensive and labor-intensive setups, often involving controlled recording environments and professional operators. The datasets that do exist are fragmented, small-scale, and cannot be easily scaled to meet the demands of real-world environments. The challenge is not the hardware itself, but the lack of a scalable, democratized way for millions of people to contribute meaningful embodied intelligence data. Frankly speaking, without the piece of the puzzle of large-scale embodied intelligence data, humans are still a long way from truly usable next-generation physical AI.

This problem is not unique to robotics. It spans the entire spectrum of physical AI. Whether it’s a humanoid robot performing complex tasks in a factory, or a VR system seamlessly reconstructing a physical space, each application relies on the same core infrastructure: a foundation of rich, accurate, real-world data. The shortage of this critical resource is impeding the training of end-to-end models, slowing down innovation, and delaying the widespread adoption of technologies that will one day redefine our relationship with the world.

Therefore, the core mission is clear: to build the foundational data infrastructure that will empower this next era of embodied intelligence. We must move beyond the limitations of centralized, costly, and inefficient data collection methods and create a new paradigm—one that allows for the scalable acquisition of the high-quality, real-world data needed to train the machines of tomorrow. This is not just a technological challenge; it is the cornerstone upon which the entire future of physical AI will be built.