{"ID":2827147,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.17661","arxiv_id":"2512.17661","title":"Vidarc: Embodied Video Diffusion Model for Closed-loop Control","abstract":"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.","short_abstract":"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, su...","url_abs":"https://arxiv.org/abs/2512.17661","url_pdf":"https://arxiv.org/pdf/2512.17661v1","authors":"[\"Yao Feng\",\"Chendong Xiang\",\"Xinyi Mao\",\"Hengkai Tan\",\"Zuyue Zhang\",\"Shuhe Huang\",\"Kaiwen Zheng\",\"Haitian Liu\",\"Hang Su\",\"Jun Zhu\"]","published":"2025-12-19T15:04:24Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}