Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918215772274688 |
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| author | Sun, Qiao Yang, Liujia Tang, Wei Huang, Wei Xu, Kaixin Chen, Yongchao Liu, Mingyu Yang, Jiange Zhu, Haoyi Wang, Yating He, Tong Chen, Yilun Dai, Xili Ye, Nanyang Gu, Qinying |
| author_facet | Sun, Qiao Yang, Liujia Tang, Wei Huang, Wei Xu, Kaixin Chen, Yongchao Liu, Mingyu Yang, Jiange Zhu, Haoyi Wang, Yating He, Tong Chen, Yilun Dai, Xili Ye, Nanyang Gu, Qinying |
| contents | While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity, difficulty of collection, and high dimensionality of embodied data fundamentally limit the alignment granularity between language and actions and exacerbate the challenge of long-horizon video generation--hindering generative models from achieving a "GPT moment" in the embodied domain. There is a naive observation: the diversity of embodied data far exceeds the relatively small space of possible primitive motions. Based on this insight, we propose a novel paradigm for world modeling--Primitive Embodied World Models (PEWM). By restricting video generation to fixed short horizons, our approach 1) enables fine-grained alignment between linguistic concepts and visual representations of robotic actions, 2) reduces learning complexity, 3) improves data efficiency in embodied data collection, and 4) decreases inference latency. By equipping with a modular Vision-Language Model (VLM) planner and a Start-Goal heatmap Guidance mechanism (SGG), PEWM further enables flexible closed-loop control and supports compositional generalization of primitive-level policies over extended, complex tasks. Our framework leverages the spatiotemporal vision priors in video models and the semantic awareness of VLMs to bridge the gap between fine-grained physical interaction and high-level reasoning, paving the way toward scalable, interpretable, and general-purpose embodied intelligence. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_20840 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Primitive Embodied World Models: Towards Scalable Robotic Learning Sun, Qiao Yang, Liujia Tang, Wei Huang, Wei Xu, Kaixin Chen, Yongchao Liu, Mingyu Yang, Jiange Zhu, Haoyi Wang, Yating He, Tong Chen, Yilun Dai, Xili Ye, Nanyang Gu, Qinying Robotics Artificial Intelligence Multimedia While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity, difficulty of collection, and high dimensionality of embodied data fundamentally limit the alignment granularity between language and actions and exacerbate the challenge of long-horizon video generation--hindering generative models from achieving a "GPT moment" in the embodied domain. There is a naive observation: the diversity of embodied data far exceeds the relatively small space of possible primitive motions. Based on this insight, we propose a novel paradigm for world modeling--Primitive Embodied World Models (PEWM). By restricting video generation to fixed short horizons, our approach 1) enables fine-grained alignment between linguistic concepts and visual representations of robotic actions, 2) reduces learning complexity, 3) improves data efficiency in embodied data collection, and 4) decreases inference latency. By equipping with a modular Vision-Language Model (VLM) planner and a Start-Goal heatmap Guidance mechanism (SGG), PEWM further enables flexible closed-loop control and supports compositional generalization of primitive-level policies over extended, complex tasks. Our framework leverages the spatiotemporal vision priors in video models and the semantic awareness of VLMs to bridge the gap between fine-grained physical interaction and high-level reasoning, paving the way toward scalable, interpretable, and general-purpose embodied intelligence. |
| title | Learning Primitive Embodied World Models: Towards Scalable Robotic Learning |
| topic | Robotics Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2508.20840 |