Paper page - Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation

Source: https://huggingface.co/papers/2606.17030 Authors:

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Abstract

Qwen-RobotWorld is a language-conditioned video world model that predicts future visual trajectories across multiple robotic domains using a double-stream diffusion transformer and embodied world knowledge corpus.

We introduce Qwen-RobotWorld, alanguage-conditioned video world modelforembodied intelligence. With natural language as a unified action interface, it predicts physically grounded future visual trajectories from current observations across robotic manipulation, autonomous driving, indoor navigation, and human-to-robot transfer. This unified formulation provides three promising application directions: synthetic data generation for policy training augmentation, scalable virtual environments for policy evaluation, and language-guided planning signals for downstream robot control. This is achieved through a three-part design: a) Double-Stream MMDiT withMLLM Action Encoding, where a 60-layerdouble-stream diffusion transformercouples frozen Qwen2.5-VL semantics withvideo-VAE latentsthroughlayer-wise joint attention; b)Embodied World Knowledge(EWK), an 8.6Mvideo-text corpus(200M+ frames) with action-language mapping over 20+ embodiments and 500+ action categories; and c) General+ExpertProgressive Curriculum, a two-stage training strategy that first learns general visual priors and then injects embodied specialization under a shared language interface. Extensive results show strong competitiveness: ranks 1st overall on EWMBench and DreamGen Bench, outperforms all open-source models on WorldModelBench and PBench. Additional zero-shot analyses on RoboTwin-IF benchmark further support robust generalization and multi-view consistency.

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