How Elon Musk’s humanoid dream clashes with 100,000-year data reality

The article discusses the significant challenges facing Elon Musk’s vision of humanoid robots, emphasizing insights from UC Berkeley roboticist Ken Goldberg. Despite advances in large language models (LLMs) trained on vast internet text, robotics lags far behind due to a massive "100,000-year data gap" in the kind of rich, embodied data required for robots to achieve human-like dexterity and reliability. Simple human tasks such as picking up a glass or changing a light bulb involve complex perception and manipulation skills that robots currently cannot replicate. Attempts to use online videos or simulations to train robots fall short because these sources lack detailed 3D motion and force data essential for fine motor skills. Teleoperation generates data but only at a linear, slow rate compared to the exponential data fueling language models. Goldberg highlights a debate in robotics between relying solely on massive data collection versus traditional engineering approaches grounded in physics and explicit world modeling. He advocates a pragmatic middle ground: deploying robots with limited but reliable capabilities to collect real-world