Article

EMMA framework uses generative visual models to improve visual generalization of VLA manipulation policies

The EMMA framework enhances vision-language-action (VLA) policies through a generative data engine and training pipeline, addressing the high cost of collecting robot manipulation data across varied appearances. Its core component, DreamTransfer, is based on a diffusion Transformer and generates multi-view-consistent, geometrically plausible manipulation videos, supporting text-driven editing of foreground, background, and lighting while preserving 3D structure.

Part of the event: EMMA Enhances VLA Policy Generalization with Generated Visual Data

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