From Semantic Understanding through Visual Foresight to Action EWAM is a unified embodied model that jointly processes vision-language, future-video, and action tokens in a single flow-matching Diffusion Transformer. Without any explicit stage design, the action stream learns a depth-wise division of labor: shallow layers retrieve instruction and current-scene semantics from the vision-language expert, intermediate layers shift toward predicted future-frame representations, and deep layers are dominated by action self-attention for motor refinement. Real-robot results on Franka, Dobot, and Unitree G1-D are reported in the technical report. Each checkpoint is a DeepSpeed-style directory…
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HaoWang
HaoWang00
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