To overcome the limitations of wheeled rovers getting stuck, future exploration robots may be inspired by plant growth. Instead of moving through space, they will 'grow' through it, extending structures from one point to another. This approach allows for traversing difficult terrain and creating a distributed information network.
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At NASA, the design process involves building multiple quick prototypes and deliberately failing them to learn their limits. This deep understanding, gained through intentional destruction, is considered essential before attempting to build the final, mission-critical version of a component like those on the Mars Rover.
While LLMs dominate headlines, Dr. Fei-Fei Li argues that "spatial intelligence"—the ability to understand and interact with the 3D world—is the critical, underappreciated next step for AI. This capability is the linchpin for unlocking meaningful advances in robotics, design, and manufacturing.
Large language models are insufficient for tasks requiring real-world interaction and spatial understanding, like robotics or disaster response. World models provide this missing piece by generating interactive, reason-able 3D environments. They represent a foundational shift from language-based AI to a more holistic, spatially intelligent AI.
Being rooted and unable to escape danger, plants evolved to be highly predictive. They must anticipate changes in light, seasons, and resources to survive. This immobility, often seen as a weakness, is actually the evolutionary driver for a sophisticated form of forward-thinking intelligence.
World Labs co-founder Fei-Fei Li posits that spatial intelligence—the ability to reason and interact in 3D space—is a distinct and complementary form of intelligence to language. This capability is essential for tasks like robotic manipulation and scientific discovery that cannot be reduced to linguistic descriptions.
Self-driving cars, a 20-year journey so far, are relatively simple robots: metal boxes on 2D surfaces designed *not* to touch things. General-purpose robots operate in complex 3D environments with the primary goal of *touching* and manipulating objects. This highlights the immense, often underestimated, physical and algorithmic challenges facing robotics.
AR and robotics are bottlenecked by software's inability to truly understand the 3D world. Spatial intelligence is positioned as the fundamental operating system that connects a device's digital "brain" to physical reality. This layer is crucial for enabling meaningful interaction and maturing the hardware platforms.
Human intelligence is multifaceted. While LLMs excel at linguistic intelligence, they lack spatial intelligence—the ability to understand, reason, and interact within a 3D world. This capability, crucial for tasks from robotics to scientific discovery, is the focus for the next wave of AI models.
Classical robots required expensive, rigid, and precise hardware because they were blind. Modern AI perception acts as 'eyes', allowing robots to correct for inaccuracies in real-time. This enables the use of cheaper, compliant, and inherently safer mechanical components, fundamentally changing hardware design philosophy.
Unlike older robots requiring precise maps and trajectory calculations, new robots use internet-scale common sense and learn motion by mimicking humans or simulations. This combination has “wiped the slate clean” for what is possible in the field.
