Google spent 7 years and billions of dollars trying to build a robot butler. It failed. 🤖 We investigate the collapse of Everyday Robots and the pivotal realization that physical data is the ultimate bottleneck for AI.

1. The Physics Barrier: We break down why LLMs can write poetry but can't pick up a cup. The "Moravec's Paradox" of robotics means that abstract reasoning is easy for AI, but basic physical interaction—friction, gravity, lighting—is exponentially harder because there is no "internet of data" for the physical world. A robot needs millions of physical trials to learn what a child learns in minutes.

2. The New Brain: We explain the breakthrough: Gemini Robotics 1.5. Instead of coding motion from scratch, Google is now "grafting" the brain of a Large Language Model onto a robot body. Using a "Vision-Language-Action" (VLA) model, the robot can now "think before it acts," using Google Search to check local recycling laws before sorting trash, bridging the gap between digital knowledge and physical action.

3. The Robot Constitution: How do you stop a learning robot from hurting someone? We discuss AutoRT and the "Robot Constitution"—a set of semantic safety rules inspired by Isaac Asimov. By prompting the LLM with strict rules like "do not interact with sharp objects," safety compliance jumped from 18% to 93%, allowing fleets of robots to wander offices unsupervised to collect data.