How AI is Bridging the Common Sense Gap in Robotics

Liquid neural networks are emerging as a breakthrough for physical robotics, addressing the “common sense gap” that limits transformer-style models in real-world tasks. According to MIT’s Daniela Rus, these adaptive architectures deliver the same steering and control capabilities with dramatically fewer resources—19 neurons and ~2,000 parameters versus ~100,000 neurons and ~500,000 parameters in traditional systems—yielding hundreds of times better energy efficiency. That efficiency and improved spatial-temporal and causal reasoning should accelerate deployment across manufacturing, healthcare and logistics. For investors, diversified ETFs offer a straightforward way to gain exposure to this secular shift; the article highlights ROBO for hardware exposure and THNQ for the intelligence layer.