World Action Models: The Next Frontier in Embodied AI
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911674287521792 |
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| author | Wang, Siyin Shi, Junhao Fu, Zhaoyang He, Xinzhe Liu, Feihong Yang, Chenchen Zhou, Yikang Fei, Zhaoye Gong, Jingjing Fu, Jinlan Shou, Mike Zheng Huang, Xuanjing Qiu, Xipeng Jiang, Yu-Gang |
| author_facet | Wang, Siyin Shi, Junhao Fu, Zhaoyang He, Xinzhe Liu, Feihong Yang, Chenchen Zhou, Yikang Fei, Zhaoye Gong, Jingjing Fu, Jinlan Shou, Mike Zheng Huang, Xuanjing Qiu, Xipeng Jiang, Yu-Gang |
| contents | Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention. A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline. We term this emerging paradigm World Action Models (WAMs): embodied foundation models that unify predictive state modeling with action generation, targeting a joint distribution over future states and actions rather than actions alone. However, the literature remains fragmented across architectures, learning objectives, and application scenarios, lacking a unified conceptual framework. We formally define WAMs and disambiguate them from related concepts, and trace the foundations and early integration of VLA and world model research that gave rise to this paradigm. We organize existing methods into a structured taxonomy of Cascaded and Joint WAMs, with further subdivision by generation modality, conditioning mechanism, and action decoding strategy. We systematically analyze the data ecosystem fueling WAMs development, spanning robot teleoperation, portable human demonstrations, simulation, and internet-scale egocentric video, and synthesize emerging evaluation protocols organized around visual fidelity, physical commonsense, and action plausibility. Overall, this survey provides the first systematic account of the WAMs landscape, clarifies key architectural paradigms and their trade-offs, and identifies open challenges and future opportunities for this rapidly evolving field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_12090 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | World Action Models: The Next Frontier in Embodied AI Wang, Siyin Shi, Junhao Fu, Zhaoyang He, Xinzhe Liu, Feihong Yang, Chenchen Zhou, Yikang Fei, Zhaoye Gong, Jingjing Fu, Jinlan Shou, Mike Zheng Huang, Xuanjing Qiu, Xipeng Jiang, Yu-Gang Robotics Computation and Language Computer Vision and Pattern Recognition Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention. A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline. We term this emerging paradigm World Action Models (WAMs): embodied foundation models that unify predictive state modeling with action generation, targeting a joint distribution over future states and actions rather than actions alone. However, the literature remains fragmented across architectures, learning objectives, and application scenarios, lacking a unified conceptual framework. We formally define WAMs and disambiguate them from related concepts, and trace the foundations and early integration of VLA and world model research that gave rise to this paradigm. We organize existing methods into a structured taxonomy of Cascaded and Joint WAMs, with further subdivision by generation modality, conditioning mechanism, and action decoding strategy. We systematically analyze the data ecosystem fueling WAMs development, spanning robot teleoperation, portable human demonstrations, simulation, and internet-scale egocentric video, and synthesize emerging evaluation protocols organized around visual fidelity, physical commonsense, and action plausibility. Overall, this survey provides the first systematic account of the WAMs landscape, clarifies key architectural paradigms and their trade-offs, and identifies open challenges and future opportunities for this rapidly evolving field. |
| title | World Action Models: The Next Frontier in Embodied AI |
| topic | Robotics Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.12090 |