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This looks like a truly brilliant launch for robot training.
Workers wear a thin 131 g sensor glove that records exact hand movements while they do ordinary tasks, so no robot is needed to collect the data.
Chinese robotics startup LiberAI (@liberai_robot) released Liber-0, the AI model that controls a robot’s arms and hands.
With no robot demonstrations of the tested tasks, Liber-0 completed 36.0% of 600 attempts at 60 new tasks. In LiberAI’s tests, 10K glove hours also beat 2M hours of internet video.
Robots normally learn a task after people steer them through it repeatedly, and Liber-0 replaces much of that with 2.2M+ hours of human experience.
Video alone shows what hands do, not the measured motion a robot must copy.
The company’s LiberSense Glove supplies that motion by logging hand poses and trajectories while people work.
Measured motion beat volume: 10K glove hours outperformed 2M hours of web video in matched runs.
Trained on 25% of the robot demonstrations plus glove data, a policy scored 75.6 for partial completion, beating 72.8 from all of them.
Introducing Liber-0, our World Action Model for zero-shot robot manipulation—including dexterous, multi-step tasks.
Across 60 tasks proposed by GPT-6 and 600 trials, Liber-0 achieved 36% overall success without robot demonstrations collected specifically for those tasks:
• Pick-and-place: 63.5%
• Other manipulation tasks: 34.0%
• Challenging multi-step tasks: 10.5%
Zero-shot capability extends beyond individual actions to coordinated sequences.
Pretrained on 2.2M+ hours of human experience, Liber-0 jointly predicts future observations and actions to turn human experience into robot skills.
Technical blog: https://t.co/X5NMTjsr8r
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