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DiT4DiT is now open source! As the first humanoid-deployable Video-Action Model built on a world model, DiT4DiT continues to surprise us. In our paper last month, we showed its strong data efficiency. Now, with only slight modifications, it enables real-time whole-body autonomous pick-and-place. Paper: Code: Website:

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Shuo Yang

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15,969 görüntüleme • 6 ay önce •via X (Twitter)

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Alfred Cueva profil fotoğrafı
Alfred Cueva6 ay önce

Really interesting work! I am also trying to use VAM for humanoid whole body control

Michael Cho - Rbt/Acc profil fotoğrafı
Michael Cho - Rbt/Acc6 ay önce

Really cool!

Tommy AutoTech profil fotoğrafı
Tommy AutoTech6 ay önce

Insert me pitching world models to my VP instead of traditional vision systems. This pick-and-place fluidity is undeniable.

𓅋 𐎫𐎤𐎶𐏀 ‎ﷺ profil fotoğrafı
𓅋 𐎫𐎤𐎶𐏀 ‎ﷺ6 ay önce

Are you training in sim? How many episodes averagely per task?

Jie Wang profil fotoğrafı
Jie Wang6 ay önce

very cool! Do you see an advantage of language steer-ability here?

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We’re excited to share DiT4DiT, an end-to-end Video-Action Model for robot learning that unifies a video Diffusion Transformer and an action Diffusion Transformer in a single cascaded framework. By leveraging the rich spatiotemporal and physical dynamics learned through video generation, rather than static image-text priors, DiT4DiT achieves state-of-the-art results on LIBERO (98.6%) and RoboCasa GR1 (50.8%) with far less training data, delivering over 10× better sample efficiency and up to 7× faster convergence. Real-world deployment on a humanoid robot further shows robust generalization. We believe this is a step toward making video generation a powerful backbone for robot policy learning. This work builds upon the brilliant foundations laid by Nvidia's GR00T and Cosmos. Project: Paper: Code: Coming soon. In the meantime, you can ask your coding agent to reproduce the method based on GR00T/Cosmos.
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We’re excited to share DiT4DiT, an end-to-end Video-Action Model for robot learning that unifies a video Diffusion Transformer and an action Diffusion Transformer in a single cascaded framework. By leveraging the rich spatiotemporal and physical dynamics learned through video generation, rather than static image-text priors, DiT4DiT achieves state-of-the-art results on LIBERO (98.6%) and RoboCasa GR1 (50.8%) with far less training data, delivering over 10× better sample efficiency and up to 7× faster convergence. Real-world deployment on a humanoid robot further shows robust generalization. We believe this is a step toward making video generation a powerful backbone for robot policy learning. This work builds upon the brilliant foundations laid by Nvidia's GR00T and Cosmos. Project: Paper: Code: Coming soon. In the meantime, you can ask your coding agent to reproduce the method based on GR00T/Cosmos.

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