MoWM: Mixture-of-World-Models for Embodied Planning via Latent-to-Pixel Feature Modulation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911435992334336 |
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| author | Yu, Yangcheng Jin, Xin Shang, Yu Zhang, Xin Su, Haisheng Wu, Wei Li, Yong |
| author_facet | Yu, Yangcheng Jin, Xin Shang, Yu Zhang, Xin Su, Haisheng Wu, Wei Li, Yong |
| contents | Embodied action planning is a core challenge in robotics, requiring models to generate precise actions from visual observations and language instructions. While video generation world models are promising, their reliance on pixel-level reconstruction often introduces visual redundancies that hinder action decoding and generalization. Latent world models offer a compact, motion-aware representation, but overlook the fine-grained details critical for precise manipulation. To overcome these limitations, we propose MoWM, a mixture-of-world-model framework that fuses representations from hybrid world models for embodied action planning. Our approach combines motion-aware latent world model features with pixel-space features, enabling MoWM to emphasize action-relevant visual details for action decoding. Extensive evaluations on the CALVIN and real-world manipulation tasks demonstrate that our method achieves state-of-the-art task success rates and superior generalization. We also provide a comprehensive analysis of the strengths of each feature space, offering valuable insights for future research in embodied planning. The code is available at: https://github.com/tsinghua-fib-lab/MoWM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoWM: Mixture-of-World-Models for Embodied Planning via Latent-to-Pixel Feature Modulation Yu, Yangcheng Jin, Xin Shang, Yu Zhang, Xin Su, Haisheng Wu, Wei Li, Yong Computer Vision and Pattern Recognition Embodied action planning is a core challenge in robotics, requiring models to generate precise actions from visual observations and language instructions. While video generation world models are promising, their reliance on pixel-level reconstruction often introduces visual redundancies that hinder action decoding and generalization. Latent world models offer a compact, motion-aware representation, but overlook the fine-grained details critical for precise manipulation. To overcome these limitations, we propose MoWM, a mixture-of-world-model framework that fuses representations from hybrid world models for embodied action planning. Our approach combines motion-aware latent world model features with pixel-space features, enabling MoWM to emphasize action-relevant visual details for action decoding. Extensive evaluations on the CALVIN and real-world manipulation tasks demonstrate that our method achieves state-of-the-art task success rates and superior generalization. We also provide a comprehensive analysis of the strengths of each feature space, offering valuable insights for future research in embodied planning. The code is available at: https://github.com/tsinghua-fib-lab/MoWM. |
| title | MoWM: Mixture-of-World-Models for Embodied Planning via Latent-to-Pixel Feature Modulation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.21797 |