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cs.RO2026
Learning Transferable Dynamics Priors from Action to World Modeling
Ze Huang, Jiahui Zhang, Hairuo Liu +3
We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict how actions drive visual sc…
cs.RO2026
From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
Yajie Li, Bozhou Zhang, Chun Gu +5
Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively exploiting these imagined fut…