RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions
RynnWorld-Latent is a video world model that uses 3D-grounded latent actions from RynnLAM as a shared conditioning interface, allowing joint pretraining on human interaction videos, robot demonstrations, and simulation rollouts without a common native action space. It combines global action tokens with an identity-initialized local pathway aligning actions to future video latents. Pretraining covers 23 datasets (about 553.5M frames), with post-training adaptation to target robots. Reported results include improved video prediction on public benchmarks and two real-robot platforms, and synthetic mid-training improving policy success on LIBERO and LIBERO-Plus.
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RynnWorld-Latent is a video world model that uses 3D-grounded latent actions from RynnLAM as a shared conditioning interface, allowing joint pretraining on human interaction videos, robot demonstrations, and simulation rollouts without a common native action space. It combines global action tokens with an identity-initialized local pathway aligning actions to future video latents. Pretraining covers 23 datasets (about 553.5M frames), with post-training adaptation to target robots. Reported results include improved video prediction on public benchmarks and two real-robot platforms, and synthetic mid-training improving policy success on LIBERO and LIBERO-Plus.
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Key findings from the stored brief
- Uses frozen RynnLAM latent-action descriptors as a shared interface across human videos, robot demos, and simulation.
- Pretrains on 23 datasets of roughly 553.5M frames before adapting to target robots via post-training.
- Synthetic mid-training improves policy success rates on LIBERO and LIBERO-Plus benchmarks.
- Both data-generation routes improve real-world policy success over real-demonstration-only training in evaluated settings.
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Why this may matter
Offers a route to learn controllable visual dynamics from heterogeneous, unlabeled video sources without aligning incompatible robot action spaces, potentially reducing reliance on costly real-robot data collection.
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