Title: Vidarc: Low Latency Embodied Video Diffusion Model with Closed-loop Control
Primary area: applications to robotics, autonomy, planning
Keywords: Robotics, Video Diffusion Model, Computer Vision

Abstract: Robotic arm manipulation in data-scarce settings remains challenging due to complex embodiment dynamics and diverse contexts. Recent video-based approaches, though adept at capturing and transferring temporal and physical interactions, are typically not optimized for the embodiment-specific closed-loop control, suffering from high latency and insufficient grounding. We present Vidarc (Video Diffusion for Action Reasoning and Closed-loop Control), an 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, our method achieves fast, accurate closed-loop control. Pre-trained on one million cross-embodiment episodes, Vidarc outperforms state-of-the-art baselines in both task success rates (at least 15% higher in real-world deployment) and latency, demonstrating robust generalization and error correction on unseen robotic platforms.