HMA is a masked autoregressive transformer that predicts future video and actions across many robot embodiments, running up to 15x faster than prior diffusion-based video simulators while matching or improving visual fidelity.
Se3-nets: Learning rigid body motion using deep neural networks
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Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression
HMA is a masked autoregressive transformer that predicts future video and actions across many robot embodiments, running up to 15x faster than prior diffusion-based video simulators while matching or improving visual fidelity.