#具身智能

Actually, AI/Robotics will mean everyone can have a penthouse if they want. The output of goods & services will be several orders of magnitude higher than today’s economy. Read the Iain Banks Culture books for the best imagining of how it will be. That said, what is the future you want? Amazing abundance seems the best to me.

Someone rented prime Times Square real estate to tease a new humanoid robot with 3 claims: - it has a "real brain," - works around the house, and - lets you "simply live." - "April 17th on X" Featuring a blurred humanoid alongside it! 🤔 https://t.co/fFfbdPHUoO

Beijing’s Humanoid Robot Half Marathon, night run test. Around 40% of teams are running fully autonomous, remote-controlled runs get a 1.2× penalty (Beijing's E-Town Half Marathon on April 19 with 100+ teams, 300+ humanoids competing) https://t.co/CKhEBlFZ3V

Robotic data is insanely expensive and brutal to collect. It’s the only thing holding back general-purpose robots right now. Figure CEO @adcock_brett : "If we could get a pile of data in the helix stack, we would solve general robotics right now." https://t.co/YWpA20gdn5

Indian factory workers wear head-mounted cameras to capture data for training robotics AI models. This image captures a blunt truth about robotics: teaching a machine to move in the real world is still painfully expensive. What looks dystopian at first is also a clue about the bottleneck. Robots do not learn useful physical behavior from internet-scale text the way language models do. They need embodied data: hands reaching, wrists turning, objects slipping, fabric folding, tools resisting, people recovering from small mistakes in real time. That data is rare because reality is slow, messy, and costly. A robot fleet is expensive to buy, expensive to maintain, hard to supervise, and dangerous to scale in uncontrolled settings. Even teleoperation is costly, because every minute of human-guided movement requires hardware, operators, calibration, and failure recovery. So companies go looking for the cheapest possible proxy for physical intelligence. First-person video from factory workers is not the same as robot action data, but it can still be valuable because it captures sequencing, posture, bimanual coordination, and the micro-adjustments that make real work look easy. The frontier in robotics is not just better models. It is better pipelines for collecting reality itself. That is why warehouses, factories, kitchens, and repair benches matter so much: they are dense environments of repeated contact with the physical world, which is exactly what robots lack. The unsettling part is that this turns human labor into training infrastructure twice over, first as work, then as data. And until embodied data becomes cheaper to gather than human motion is to record, robotics will keep learning from workers before it fully replaces them.

Tesla driving itself around LA

http://x.com/i/article/2041371538482761728

Google DeepMind 🤝 Agile Robots Our new research partnership will integrate the Gemini foundation models with their hardware to help build the next generation of more helpful and useful robots. Find out more → https://goo.gle/4lKu7de https://t.co/bzPUUvPiJp

Our recent findings on World Action Models (WAMs): the core advantage of WAMs is not test-time “imagination” of futures, but the training-time supervision from future video prediction. We propose Fast-WAM, which makes inference simple, fast, and policy-centric. https://t.co/lNSdK7rwvm