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ETNews details Xiaomi's humanoid running three concrete production tasks - the embodied race gets a China-side number breakdown.
Xiaomi has published performance numbers for the "intern" humanoid robot deployed in its EV factory. On the original task - mounting nuts on car bodies - the success rate reached 98%, up from 90.2%. On two new tasks it has since been assigned (sorting interior parts and folding reusable logistics boxes), the robot logs "above 90%".
ETNews reports (18 July) that Xiaomi has broken down the performance of the humanoid robot it introduced into its EV plant earlier this year. The headline number - 98% success on nut-assembly on car bodies, up from an initial 90.2% - comes with Xiaomi's own framing that this is within 1 percentage point of skilled human workers. Since that ramp, the robot has been assigned two additional tasks: sorting interior parts, and folding reusable logistics boxes for internal supply. Both new tasks are logged at above 90% success. Xiaomi says its next work is on force-controlled manipulation.
The embodied-foundation-race framing matters here. LimX Dynamics, X-Square Robot and Nvidia GR00T have all been claiming that a single "backbone" model will unlock robotics the way LLMs unlocked language. The counter - from Figure and Agility Robotics - has been that industrial reliability requires purpose-built stacks, not general backbones. Xiaomi's disclosure tilts the argument in a specific direction: on scoped, repetitive assembly tasks inside a plant Xiaomi controls, the foundation-model path can hit near-human numbers. On new tasks it hits 90%+ from the start - not 98% - which is the honest data point to hold.
Three implications. For industrial automation buyers, this is the first China-side data point comparable to Figure's Boeing/BMW work - the "does it work in a real plant?" question now has a task-by-task answer, not a single number. For the embodied race, the vertical-integration model (own factory, own robot, own foundation model, own data pipeline) is the one showing the fastest ramp on scoped tasks; that's a headwind for third-party embodied platforms that need to sell into someone else's plant with someone else's data. For rivals - particularly Agility Robotics and Figure - the challenge is now to publish equivalent breakdowns in equivalent environments. Watch for two follow-ups: independent verification of the 98% figure, and how quickly the two 90%+ tasks are pushed toward the same near-human threshold. That ramp curve is the real signal, not the headline.
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I'm curious about the robot's adaptability. How well can it handle unexpected changes in the assembly process or new types of nuts?
I wonder how these robots handle quality control. Can they detect defects as well as human workers?
Robots can detect defects but may struggle with nuanced quality issues that humans can spot.
I wonder how the robot handles maintenance and downtime. What's the lifespan of these intern robots and their components?
Impressive stats! I wonder how the robot's performance compares to human workers in terms of speed and efficiency.
It's also worth considering the robot's adaptability to new tasks compared to humans.
Intriguing! I'd love to know more about the robot's learning process and how it handles errors during assembly.
Fascinating progress! I wonder how adaptable these robots are to more complex tasks beyond assembly.
Fascinating! I wonder how these robots handle quality control and ensure consistency in their tasks.
Great to see advancements in robotics! Curious about the energy consumption and environmental impact of these humanoid robots.
Impressive efficiency gains, but I wonder about the long-term implications for human workers.
Course aux modèles fondation embodied : X-Square, Xiaomi, GR00T