Video Friday: Do We Need Superhuman Humanoid Robots?
20 Aug 2026 4 min read CFD contour plot of flow velocity in a complex mechanical housing cross-section ComputingSemiconductorsTransportationConsumer ElectronicsClimate ChangeSponsored Article ## Identifying the Root Cause of Electronics Failures With Simula...
Key takeaways
- The most recent headlines on humanoid robots show a surge of activity across several regions.
- In Kazakhstan, officials announced the construction of a full‑cycle humanoid‑robot factory, with AgiBot and the local QAZBOT team demonstrating robots that can dance, perform martial‑arts moves and interact with humans as part of a drive to build a domestic robotics and AI industry.
- In China, the World Humanoid Robot Games in Beijing have become an annual showcase; this year’s opening featured the “Lightning” robot from Honor running 100 m in 9.32 seconds—faster than Usain Bolt’s 9.58‑second record—and a standing high‑jump of 2.88 m, both eclipsing human marks.
- The same event highlighted over 2,000 humanoids competing in races, table‑tennis, soccer and other tasks, underscoring China’s rapid progress despite new U.S. import bans on foreign‑made humanoids.
- Meanwhile, Chinese firm Unitree Robotics celebrated a blockbuster Shanghai IPO, its shares closing more than 460 % above the offering price, and its founder warned that a “ChatGPT moment” for humanoid robots could still be a decade away because the machines still struggle with task generalisation and precise movement.
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Humans have a remarkable ability to perform new physical skills from only one or a few examples. Our latest robot foundation model, GEN-1.5, exhibits the beginnings of that same ability: it can learn a new task in seconds, from a single example, without gradient updates or fine-tuning. It displays broad capabilities across one-shot and few-shot learning from demonstration, as well as zero-shot physical generalization. Although the tasks are simple and short-horizon, this is the first model we know for which one-shot and few-shot learning of physical skills have emerged at scale. We view these results as a significant step towards our mission of building general intelligence for the physical world.
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