iFLYTEK-Embodied-Omni Technical Report

# iFLYTEK-Embodied-Omni Technical Report
Source: [https://arxiv.org/abs/2607.02542](https://arxiv.org/abs/2607.02542)
Authors:[Yuan Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y),[Jingfei Ni](https://arxiv.org/search/cs?searchtype=author&query=Ni,+J),[Guanchen Lu](https://arxiv.org/search/cs?searchtype=author&query=Lu,+G),[Shiqi Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+S),[Qingshan Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+Q),[Chi Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+C),[Xin Nie](https://arxiv.org/search/cs?searchtype=author&query=Nie,+X),[Wenjie Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+W),[Lin Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+L),[Zhiyuan Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+Z),[Mingxin Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+M),[Jiajia Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+J),[Diyuan Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+D),[Jia Pan](https://arxiv.org/search/cs?searchtype=author&query=Pan,+J),[Chao Ji](https://arxiv.org/search/cs?searchtype=author&query=Ji,+C)

[View PDF](https://arxiv.org/pdf/2607.02542)

> 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\.

## Submission history

From: Yuan Zhang \[[view email](https://arxiv.org/show-email/d6dca6f9/2607.02542)\] **\[v1\]**Wed, 24 Jun 2026 00:25:44 UTC \(3,114 KB\)