DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

• URL: http://arxiv.org/abs/2602.14974v1
• Date: Mon, 16 Feb 2026 17:59:16 GMT
• Title: DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI
• Abstract summary: We present DM0, an Embodied-Native Vision-Language-Action framework designed for Physical AI.<n>Our methodology follows a comprehensive three-stage pipeline: Pretraining, Mid-Training, and Post-Training.<n>Experiments on the RoboChallenge benchmark demonstrate that DM0 achieves state-of-the-art performance in both Specialist and Generalist settings.
• Score: 84.9072161615971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed for Physical AI. Unlike approaches that treat physical grounding as a fine-tuning afterthought, DM0 unifies embodied manipulation and navigation by learning from heterogeneous data sources from the onset. Our methodology follows a comprehensive three-stage pipeline: Pretraining, Mid-Training, and Post-Training. First, we conduct large-scale unified pretraining on the Vision-Language Model (VLM) using diverse corpora--seamlessly integrating web text, autonomous driving scenarios, and embodied interaction logs-to jointly acquire semantic knowledge and physical priors. Subsequently, we build a flow-matching action expert atop the VLM. To reconcile high-level reasoning with low-level control, DM0 employs a hybrid training strategy: for embodied data, gradients from the action expert are not backpropagated to the VLM to preserve generalized representations, while the VLM remains trainable on non-embodied data. Furthermore, we introduce an Embodied Spatial Scaffolding strategy to construct spatial Chain-of-Thought (CoT) reasoning, effectively constraining the action solution space. Experiments on the RoboChallenge benchmark demonstrate that DM0 achieves state-of-the-art performance in both Specialist and Generalist settings on Table30.

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