iFLYTEK-Embodied-Omni Technical Report

Yuan Zhang; Jingfei Ni; Guanchen Lu; Shiqi Zhang; Qingshan Xu; Chi Liu; Xin Nie; Wenjie Xu; Lin Gao; Zhiyuan Cheng; Mingxin Zhou; Jiajia Wu; Diyuan Liu; Jia Pan; Chao Ji

arXiv · 2607.02542

iFLYTEK-Embodied-Omni Technical Report

Yuan Zhang·Jingfei Ni·Guanchen Lu·Shiqi Zhang·Qingshan Xu·Chi Liu·Xin Nie·Wenjie Xu·Lin Gao·Zhiyuan Cheng·Mingxin Zhou·Jiajia Wu·Diyuan Liu·Jia Pan·Chao Ji

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Abstract

General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons. Existing approaches typically specialize in visual-language reasoning, video-based world modeling, or action generation, while cascaded pipelines that first synthesize future observations and then infer actions can introduce interface bottlenecks and compound prediction errors. We present iFLYTEK-Embodied-Omni, a unified multimodal foundation model that jointly models vision(videos and images), language, and action within a single Omni framework. Its modality-specific visual-language, video-generation, and action-generation components communicate through shared multimodal self-attention. This design establishes brain-cerebellum collaboration: the vision-language modeland video generation model form a high-level brain for instruction understanding, task planning, progress tracking, and future visual-state prediction, whereas the action generation modelserves as a low-level cerebellum that directly converts planned subgoals and shared multimodal context into executable action chunks. To develop these capabilities, we combine action-annotated and action-free embodied videos from human demonstrations and robot interactions with embodied reasoning, embodied perception, and general-purpose image-text data to construct a comprehensive dataset. We further adopt a four-stage strategy that progressively trains the VLM, VGM, and AGM before jointly fine-tuning the complete model.

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Yuan Zhang, Jingfei Ni, Guanchen Lu, Shiqi Zhang, Qingshan Xu, Chi Liu, Xin Nie, Wenjie Xu, Lin Gao, Zhiyuan Cheng, Mingxin Zhou, Jiajia Wu, Diyuan Liu, Jia Pan, Chao Ji. 2026-06-24. iFLYTEK-Embodied-Omni Technical Report. https://arxiv.org/abs/2607.02542

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