Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Authors: name: Jie Zhang, name: Xiaoyue Chen, name: Anzhe Chen, name: Chenxu Lv, name: Deqing Li, name: Gengze Zhou, name: Hang Yin, name: Haoqi Yuan, name: Haoyang Li, name: Jiahao Li, name: Jiazhao Zhang, name: Jingren Zhou, name: Kaiyuan Gao, name: Kun Yan, name: Lihan Jiang, name: Ningyuan Tang, name: Pei Lin, name: Qihang Peng, name: Shengming Yin, name: Tianhe Wu, name: Tianyi Yan, name: Xiao Xu, name: Yan Shu, name: Yanran Zhang, name: Ye Wang, name: Yi Wang, name: Yilei Chen, name: Yixian Xu, name: Yiyang Huang, name: Yuxiang Chen, name: Zekai Zhang, name: Zhendong Wang, name: Zhixing Lei, name: Zhixuan Liang, name: Zihao Liu, name: Zikai Zhou, name: Xiong-Hui Chen, name: Chenfei Wu
Organization: Qwen

We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied 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 with MLLM Action Encoding, where a 60-layer double-stream diffusion transformer couples frozen Qwen2.5-VL semantics with video-VAE latents through layer-wise joint attention; b) Embodied World Knowledge (EWK), an 8.6M video-text corpus (200M+ frames) with action-language mapping over 20+ embodiments and 500+ action categories; and c) General+Expert Progressive 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.