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.
@article{arxiv.2512.17661,
title = {Vidarc: Embodied Video Diffusion Model for Closed-loop Control},
author = {Yao Feng and Chendong Xiang and Xinyi Mao and Hengkai Tan and Zuyue Zhang and Shuhe Huang and Kaiwen Zheng and Haitian Liu and Hang Su and Jun Zhu},
journal= {arXiv preprint arXiv:2512.17661},
year = {2025}
}