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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.17661 |
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| _version_ | 1866918256275619840 |
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| author | Feng, Yao Xiang, Chendong Mao, Xinyi Tan, Hengkai Zhang, Zuyue Huang, Shuhe Zheng, Kaiwen Liu, Haitian Su, Hang Zhu, Jun |
| author_facet | Feng, Yao Xiang, Chendong Mao, Xinyi Tan, Hengkai Zhang, Zuyue Huang, Shuhe Zheng, Kaiwen Liu, Haitian Su, Hang Zhu, Jun |
| contents | Robotic arm manipulation in data-scarce settings is a highly challenging task due to the complex embodiment dynamics and diverse contexts. Recent video-based approaches have shown great promise in capturing and transferring the temporal and physical interactions by pre-training on Internet-scale video data. However, such methods are often not optimized for the embodiment-specific closed-loop control, typically suffering from high latency and insufficient grounding. In this paper, we present Vidarc (Video Diffusion for Action Reasoning and Closed-loop Control), a novel autoregressive embodied video diffusion approach augmented by a masked inverse dynamics model. By grounding video predictions with action-relevant masks and incorporating real-time feedback through cached autoregressive generation, Vidarc achieves fast, accurate closed-loop control. Pre-trained on one million cross-embodiment episodes, Vidarc surpasses state-of-the-art baselines, achieving at least a 15% higher success rate in real-world deployment and a 91% reduction in latency. We also highlight its robust generalization and error correction capabilities across previously unseen robotic platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vidarc: Embodied Video Diffusion Model for Closed-loop Control Feng, Yao Xiang, Chendong Mao, Xinyi Tan, Hengkai Zhang, Zuyue Huang, Shuhe Zheng, Kaiwen Liu, Haitian Su, Hang Zhu, Jun Robotics Machine Learning Robotic arm manipulation in data-scarce settings is a highly challenging task due to the complex embodiment dynamics and diverse contexts. Recent video-based approaches have shown great promise in capturing and transferring the temporal and physical interactions by pre-training on Internet-scale video data. However, such methods are often not optimized for the embodiment-specific closed-loop control, typically suffering from high latency and insufficient grounding. In this paper, we present Vidarc (Video Diffusion for Action Reasoning and Closed-loop Control), a novel autoregressive embodied video diffusion approach augmented by a masked inverse dynamics model. By grounding video predictions with action-relevant masks and incorporating real-time feedback through cached autoregressive generation, Vidarc achieves fast, accurate closed-loop control. Pre-trained on one million cross-embodiment episodes, Vidarc surpasses state-of-the-art baselines, achieving at least a 15% higher success rate in real-world deployment and a 91% reduction in latency. We also highlight its robust generalization and error correction capabilities across previously unseen robotic platforms. |
| title | Vidarc: Embodied Video Diffusion Model for Closed-loop Control |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2512.17661 |