LiberAI introduces Liber-0, a world action model pretrained on more than 2.2 million hours of human experience to support zero-shot robot manipulation. In an evaluation of 60 tasks proposed by GPT-6, it achieved 36.0% overall success without robot demonstrations collected specifically for those target tasks. The model combines large-scale human video, glove-recorded hand motion and embodiment alignment to transfer human manipulation experience to robots.

Liber-0 achieves 36.0% overall success in a zero-shot evaluation of 60 tasks proposed by GPT-6.

LiberAI has introduced Liber-0, a world action model (WAM) pretrained on more than 2.2 million hours of human experience. The Beijing-based startup said the model combines human video with glove-recorded motion and embodiment-conditioned learning to transfer human manipulation experience to robots.

A central test of Liber-0’s generalization is whether it can perform target tasks without robot demonstrations collected specifically for them. LiberAI evaluated the model on a separate suite of 60 GPT-6-proposed tasks, distinct from its internal scaling benchmarks: 20 pick-and-place (PnP) tasks, 20 non-PnP manipulation tasks, and 20 challenging non-PnP and long-horizon tasks.

Examples from the suite include placing a pen in a pencil case, pushing a drawer closed, and a longer sequence in which a pen is placed in a pouch and the pouch is stored in a drawer.

LiberAI reported task success rates of 63.5% on pick-and-place tasks, 34.0% on non-pick-and-place manipulation tasks and 10.5% on challenging non-pick-and-place and long-horizon tasks, with an overall success rate of 36.0% across 600 trials.

Here, zero-shot means the evaluated checkpoint was not trained on robot demonstrations collected specifically for these target tasks; pretraining still included robot data.

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