iFLYTEK-Embodied-Omni Technical Report
iFLYTEK-Embodied-Omni is a unified multimodal foundation model that jointly models vision, language, and action. It employs a brain-cerebellum architecture: a vision-language model and video generation model serve as the high-level brain for understanding and planning, while an action generation model acts as the low-level cerebellum for executing actions. The model is trained via a four-stage strategy on a comprehensive dataset combining human demonstrations, robot interactions, and general image-text data.
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[Submitted on 24 Jun 2026]
Title:iFLYTEK-Embodied-Omni Technical Report
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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.
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.02542 [cs.AI]
(or arXiv:2607.02542v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.02542
arXiv-issued DOI via DataCite
Submission history
From: Yuan Zhang [view email] [v1] Wed, 24 Jun 2026 00:25:44 UTC (3,114 KB)
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