Learning controllable world dynamics from heterogeneous interaction videos is challenging because robot embodiments use incompatible action spaces, while human videos lack native control labels. We pr
RynnWorld-Latent learns cross-embodiment world dynamics via 3D-grounded latent actions, enabling joint pretraining on diverse video sources and synthetic data generation for improved robot policy performance.
Learning controllable world dynamics from heterogeneous interaction videos is challenging because robot embodiments use incompatible action spaces, while human videos lack native control labels. We present RynnWorld-Latent, a cross-embodiment video world model that uses the 3D-grounded latent actions introduced by RynnLAM as a shared conditioning interface. This representation enables joint pretraining on human interaction videos, robot demonstrations, and simulation rollouts without requiring a common native action space. Built on a pretrained video transformer, RynnWorld-Latent combines global action tokens with an identity-initialized local pathway that aligns actions with future video…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 23 checks on 3 different days. Its best position was #20 of 30, reached on Oct 30, 03:28 UTC. It dropped off the list after Oct 30, 03:28 UTC.
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It was first seen on Oct 28, 2026 and has been on the list on 3 days (23 checks) since.