Robot world models stop aligning joints: shared latent actions turn human video into training data
RynnWorld-Latent conditions a video world model on continuous latent actions extracted by a frozen RynnLAM encoder, giving human, robot and simulation video one shared interface without aligning native control spaces. Pretrained on 23 datasets totaling about 553.5M frames and then post-trained per target robot, it reports better video-prediction metrics on public benchmarks and two real platforms, and data from latent-action synthesis and digital teleoperation raises downstream policy success in the evaluated settings.